Publications

Conference papers

AAAI Conference on Artificial Intelligence (AAAI)

• Tuc Van Nguyen, James Michels, Hua Shen, and Thai Le. NOMATTERXAI: Generating “No Matter What” Alterfactual Examples for Explaining Black-Box Text Classification Models. In AAAI Conference on Artificial Intelligence (AAAI), volume 39, 24939–24947, 2025.

• Caleb Schultz Kisby, Saúl A Blanco, and Lawrence S Moss. What do hebbian learners learn? reduction axioms for iterated hebbian learning. In AAAI Conference on Artificial Intelligence (AAAI), volume 38, 14894–14901, 2024.
• Eric Xing, Saranya Venkatraman, Thai Le, and Dongwon Lee. Alison: Fast and effective stylometric authorship obfuscation. In AAAI Conference on Artificial Intelligence (AAAI), volume 38, 19315–19322, 2024.

• Sam Goree, Weslie Khoo, and David J. Crandall. Correct for Whom? Subjectivity and the Evaluation of Personalized Image Aesthetics Assessment Models. In AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Jinqi Xiao, Chengming Zhang, Yu Gong, Miao Yin, Yang Sui, Lizhi Xiang, Dingwen Tao, and Bo Yuan. HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks. In AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Nikolai Karpov and Qin Zhang. Communication-efficient collaborative best arm identification. In AAAI Conference on Artificial Intelligence (AAAI), volume 37, 8203–8210, 2023.

• Nikolai Karpov and Qin Zhang. Instance-sensitive algorithms for pure exploration in multinomial logit bandit. In AAAI Conference on Artificial Intelligence (AAAI), 2022.
• Mohsen Heidari, Ananth Grama, and Wojciech Szpankowski. Toward physically realizable quantum neural networks. In AAAI Conference on Artificial Intelligence (AAAI), volume 36, 6902–6909, 2022.

• Seung Lee, Bradford Mott, Anne Ottenbreit-Leftwich, Adam Scribner, Sandra Taylor, Kyungjin Park, Jonathan Rowe, Krista Glazewski, Cindy E Hmelo-Silver, and James Lester. AI-infused collaborative inquiry in upper elementary school: A game-based learning approach. In AAAI Conference on Artificial Intelligence (AAAI), volume 35, 15591–15599, 2021.

• Caleb Kisby, Saul Blanco, Alex Kruckman, and Lawrence Moss. Logics for sizes with union or intersection. In AAAI Conference on Artificial Intelligence (AAAI), 2020.
• Kyle Richardson, Hai Hu, Lawrence S. Moss, and Ashish Sabharwal. Probing Natural Language Inference Models through Semantic Fragments. In AAAI Conference on Artificial Intelligence (AAAI), 2020.
• Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, and Filippo Menczer. Scalable and generalizable social bot detection through data selection. In AAAI Conference on Artificial Intelligence (AAAI), volume 34, 1096–1103, 2020.

AAAI Conference on Human Computation and Crowdsourcing

• Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Dongwon Lee, and others. Does human collaboration enhance the accuracy of identifying llm-generated deepfake texts? In AAAI Conference on Human Computation and Crowdsourcing, volume 11, 163–174, 2023.

AAAI International Conference on Weblogs and Social Media (ICWSM)

• Yiran Ye, Thai Le, and Dongwon Lee. NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2025.
• Manita Pote, Tuğrulcan Elmas, Alessandro Flammini, and Filippo Menczer. Coordinated Reply Attacks in Influence Operations: Characterization and Detection. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 19, 1586–1598, 2025.
• Ozgur Can Seckin, Manita Pote, Alexander C Nwala, Lake Yin, Luca Luceri, Alessandro Flammini, and Filippo Menczer. Labeled datasets for research on information operations. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 19, 2567–2574, 2025.

• Alexander Liu, Siqi Wu, and Paul Resnick. How to Train Your YouTube Recommender to Avoid Unwanted Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 18, 930–942, 2024.
• Siqi Wu and Paul Resnick. Calibrate-extrapolate: Rethinking prevalence estimation with black box classifiers. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 18, 1634–1647, 2024.
• Wooyong Jung, Nishant Asati, Phuong Lucy Doan, Thai Le, Aiping Xiong, and Dongwon Lee. The Strange Case of Jekyll and Hyde: Analysis of r/ToastMe and r/RoastMe Users on Reddit. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 18, 787–799, 2024.
• Wanying Zhao, Siyi Guo, Kristina Lerman, and Yong-Yeol Ahn. Discovering collective narratives shifts in online discussions. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 18, 1804–1817, 2024.

• Juergen Pfeffer, Daniel Matter, Kokil Jaidka, Onur Varol, Afra Mashhadi, Jana Lasser, Dennis Assenmacher, Siqi Wu, Diyi Yang, Cornelia Brantner, and others. Just another day on Twitter: a complete 24 hours of Twitter data. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 17, 1073–1081, 2023.
• Rachith Aiyappa, Matthew R DeVerna, Manita Pote, Bao Tran Truong, Wanying Zhao, David Axelrod, Aria Pessianzadeh, Zoher Kachwala, Munjung Kim, Ozgur Can Seckin, and others. A multi-platform collection of social media posts about the 2022 US midterm elections. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 17, 981–989, 2023.

• JooYoung Lee, Siqi Wu, Ali Mert Ertugrul, Yu-Ru Lin, and Lexing Xie. Whose advantage? measuring attention dynamics across youtube and twitter on controversial topics. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 16, 573–583, 2022.

• Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong, Alessandro Flammini, and Filippo Menczer. Uncovering coordinated networks on social media. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2021.

• Pik-Mai Hui, Kai-Cheng Yang, Christopher Torres-Lugo, and Filippo Menczer. BotSlayer: DIY real-time influence campaign detection. In AAAI International Conference on Weblogs and Social Media (ICWSM), volume 14, 980–982, 2020.

ACM CHI Conference on Human Factors in Computing Systems (CHI)

• Long-Jing Hsu, Janice Bays, Manasi Swaminathan, Weslie Khoo, Hiroki Sato, Kyrie Jig Amon, Sathvika Dobbala, Min Min Thant, Alex Foster, Kate Tsui, Philip B. Stafford, David J. Crandall, and Selma Sabanovic. Bittersweet Snapshots of Life: Designing to Address Complex Emotions in a Reminiscence Interaction between Older Adults and a Robot. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2025.
• Long-Jing Hsu, Alex Foster, Selma Sabanovic, and Chia-Fang Chung. Designing with dynamics: Reflections on co-design workshops between people living with dementia and their care partners. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 1–16, 2025.

• Juan F Maestre, Daria V Groves, Megan Furness, and Patrick C Shih. "It’s like With the Pregnancy Tests": Co-design of Speculative Technology for Public HIV-related Stigma and its Implications for Social Media. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 1–21, 2023.

• Varoon Mathur, Caitlin Lustig, and Elizabeth Kaziunas. Disordering Datasets: Sociotechnical Misalignments in AI-Mediated Behavioral Health. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2022.

• Sam Goree, Bardia Doosti, David J. Crandall, and Norman Su. Investigating the Homogenization of Web Design: A Mixed-Methods Approach. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2021.
• Youngseung Jeon, Seungwan Jin, Patrick C Shih, and Kyungsik Han. FashionQ: an AI-driven creativity support tool for facilitating ideation in fashion design. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2021.
• Maria D. Molina, S Shyam Sundar, Md Main Uddin Rony, Naeemul Hassan, Thai Le, and Dongwon Lee. Does clickbait actually attract more clicks? Three clickbait studies you must read. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 1–19, 2021.

ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work

• Weslie Khoo, Long-Jing Hsu, Frangil M. Ramirez, Liang Jhen Huang, Trisha Konkimalla, Chia-Fang Chung, and David J. Crandall. Bridging Human Intuition and AI in Colorful Food Assessment. In ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work, 2025.
• Houda Elmimouni and Selma Sabanovic. " I Am There But...": Remote Students’ Experiences of Inclusion through Telepresence Robots. In ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work, 1–7, 2025.

• Matthew Peter Aylett, Randy Gomez, Eleanor Sandry, and Selma Sabanovic. Unsocial robots: how western culture dooms consumer social robots to a society of one. In ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work, 1–6, 2023.

• Houda Elmimouni, John Paulin Paulin Hansen, Susan Herring, James Marcin, Marta Orduna, Pablo Perez, Irene Rae, Janet Read, Jennifer Rode, Selma Sabanovic, and others. Emerging telepresence technologies in hybrid learning environments. In ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work, 1–5, 2022.

ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

• Sam Goree, Gabriel Appleby, David J. Crandall, and Norman Su. Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper. In ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2024.

ACM Conference on Data and Application Security and Privacy

• Elisa Bertino, Murat Kantarcioglu, Cuneyt Gurcan Akcora, Sagar Samtani, Sudip Mittal, and Maanak Gupta. AI for Security and Security for AI. In ACM Conference on Data and Application Security and Privacy, 333–334, 2021.

ACM Conference on Fairness, Accountability, and Transparency (FAccT)

• Samuel Goree, Jackson Domingo, and David J. Crandall. Human-Centered Evaluation of Aesthetic Quality Assessment Models Using a Smartphone Camera Application. In ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2025.
• Alicia Freel, Sabid Bin Habib Pias, Selma Šabanović, and Apu Kapadia. How Misclassification Severity and Timing Influence User Trust in AI Image Classification: User Perceptions of High-and Low-Stakes Contexts. In ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2906–2923, 2025.

ACM Conference on Recommender Systems

• Antonela Tommasel and Filippo Menczer. Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders? In ACM Conference on Recommender Systems, 550–555, 2022.

ACM Conference on User Modeling, Adaptation and Personalization

• Antonela Tommasel, Juan Manuel Rodriguez, and Filippo Menczer. Following the Trail of Fake News Spreaders in Social Media: A Deep Learning Model. In ACM Conference on User Modeling, Adaptation and Personalization, 29–34, 2022.

ACM Designing Interactive Systems Conference (DIS)

• Long-Jing Hsu, Janice Bays, Manasi Swaminathan, Weslie Khoo, Hiroki Sato, Kyrie Jig Amon, Sathvika Dobbala, Min Min Thant, Alex Foster, Katherine M. Tsui, Philip B. Stafford, David J. Crandall, and Selma Sabanovic. Research as care: A reflection on incorporating the ethics of care in design research with people living with dementia. In ACM Designing Interactive Systems Conference (DIS), 2025.
• Aswati Panicker, Chia-Fang Chung, and Selma Šabanović. Haru in the Kitchen: Investigating Family Members’ Perceptions Toward a Social Robot Mediator of Food Experiences. In ACM Designing Interactive Systems Conference (DIS), 222–235, 2025.

• Long-Jing Hsu, Janice K Bays, Katherine M Tsui, and Selma Sabanovic. Co-designing social robots with people living with dementia: Fostering identity, connectedness, security, and autonomy. In ACM Designing Interactive Systems Conference (DIS), 2672–2688, 2023.
• Houda Elmimouni, Cooper Young, Selma Sabanovic, and Jennifer A Rode. Does Robotic Telepresence Make the Classroom Accessible? In ACM Designing Interactive Systems Conference (DIS), 194–197, 2023.

ACM Interaction Design and Children Conference

• Leigh M Levinson, Elmira Yadollahi, Bengisu Cagiltay, Shyamli Suneesh, Vicky Charisi, Angela Colvert, Kruakae Pothong, and Selma Sabanovic. Designing Playful and Ethical Child-AI Systems. In ACM Interaction Design and Children Conference, 1246–1248, 2025.
• Leigh M Levinson, Bengisu Cagiltay, Selma Sabanovic, and Bilge Mutlu. Towards a Roboticist’s Practical Guide to Working with Children. In ACM Interaction Design and Children Conference, 979–983, 2025.

• Leigh M Levinson, Nida Itrat Abbasi, Selma Sabanovic, and Hatice Gunes. Expert insights on robots for safeguarding children: How (not) and why (not)? In ACM Interaction Design and Children Conference, 600–611, 2024.

ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

• Tingyi Wanyan, Mingquan Lin, Eyal Klang, Kartikeya M Menon, Faris F Gulamali, Ariful Azad, Yiye Zhang, Ying Ding, Zhangyang Wang, Fei Wang, and others. Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction. In ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 1–9, 2022.

• Anuj Godase, Md Khaledur Rahman, and Ariful Azad. GNNfam: utilizing sparsity in protein family predictions using graph neural networks. In ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 1–10, 2021.

ACM International Conference on Knowledge Discovery and Data Mining (KDD)

• Pengtao Dang, Haiqi Zhu, Tingbo Guo, Changlin Wan, Tong Zhao, Paul Salama, Yijie Wang, Sha Cao, and Chi Zhang. Generalized matrix local low rank representation by random projection and submatrix propagation. In ACM International Conference on Knowledge Discovery and Data Mining (KDD), 390–401, 2023.

• Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, and Noseong Park. Large-scale data-driven airline market influence maximization. In ACM International Conference on Knowledge Discovery and Data Mining (KDD), 914–924, 2021.

• Thai Le, Suhang Wang, and Dongwon Lee. GRACE: Generating concise and informative contrastive sample to explain neural network model's prediction. In ACM International Conference on Knowledge Discovery and Data Mining (KDD), 238–248, 2020.

ACM International Conference on Supercomputing (SC)

• Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, and others. Clicktrain: Efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning. In ACM International Conference on Supercomputing (SC), 2021.

ACM International Web Science Conference (WebSci)

• Kai-Cheng Yang and Filippo Menczer. Accuracy and political bias of news source credibility ratings by large language models. In ACM International Web Science Conference (WebSci), 127–137, 2025.
• Lucas Raniére Juvino Santos, Leandro Balby Marinho, Claudio Elizio Calazans Campelo, Filippo Menczer, and Alessandro Flammini. Can Large Language Models Effectively Mitigate Polarization in Social Media Text? In ACM International Web Science Conference (WebSci), 348–357, 2025.

• Haewoon Kwak, Jisun An, and Yong-Yeol Ahn. A systematic media frame analysis of 1.5 million new york times articles from 2000 to 2017. In ACM International Web Science Conference (WebSci), 305–314, 2020.

ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

• Lizhi Xiang, Miao Yin, Chengming Zhang, Aravind Sukumaran-Rajam, P Sadayappan, Bo Yuan, and Dingwen Tao. TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition. In ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2022.

ACM SIGSAC Conference on Computer and Communications Security (CCS)

• Xiaoyi Chen, Siyuan Tang, Rui Zhu, Shijun Yan, Lei Jin, Zihao Wang, Liya Su, Zhikun Zhang, XiaoFeng Wang, and Haixu Tang. The janus interface: How fine-tuning in large language models amplifies the privacy risks. In ACM SIGSAC Conference on Computer and Communications Security (CCS), 1285–1299, 2024.

• Yizheng Chen, Shiqi Wang, Yue Qin, Xiaojing Liao, Suman Jana, and David Wagner. Learning security classifiers with verified global robustness properties. In ACM SIGSAC Conference on Computer and Communications Security (CCS), 477–494, 2021.

• Tao Lv, Ruishi Li, Yi Yang, Kai Chen, Xiaojing Liao, XiaoFeng Wang, Peiwei Hu, and Luyi Xing. Rtfm! automatic assumption discovery and verification derivation from library document for api misuse detection. In ACM SIGSAC Conference on Computer and Communications Security (CCS), 1837–1852, 2020.

ACM Symposium on Eye Tracking Research and Applications (ETRA)

• Zehua Zhang, David J. Crandall, Michael Proulx, Sachin Talathi, and Abhishek Sharma. Can Gaze Inform Egocentric Action Recognition? In ACM Symposium on Eye Tracking Research and Applications (ETRA), 2022.

ACM Symposium on Parallelism in Algorithms and Architectures

• Nikolai Karpov and Qin Zhang. Parallel best arm identification in heterogeneous environments. In ACM Symposium on Parallelism in Algorithms and Architectures, 53–64, 2024.

ACM Technical Symposium on Computer Science Education (SIGCSE)

• Gloria Ashiya Katuka, Srijita Chakraburty, Hyejeong Lee, Sunny Dhama, Toni Earle-Randell, Mehmet Celepkolu, Kristy Elizabeth Boyer, Krista Glazewski, Cindy Hmelo-Silver, and Tom Mcklin. Integrating natural language processing in middle school science classrooms: An experience report. In ACM Technical Symposium on Computer Science Education (SIGCSE), 639–645, 2024.

• Abe Leite and Saúl A Blanco. Effects of human vs. automatic feedback on students' understanding of AI concepts and programming style. In ACM Technical Symposium on Computer Science Education (SIGCSE), 2020.

ACM/IEEE Design Automation Conference (DAC)

• Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang, Hausi Muller, Fan Chen, and Lei Jiang. TITAN: A fast and distributed large-scale trapped-ion NISQ computer. In ACM/IEEE Design Automation Conference (DAC), 1–6, 2024.

• Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, and Dingwen Tao. Rtmobile: Beyond real-time mobile acceleration of rnns for speech recognition. In ACM/IEEE Design Automation Conference (DAC), 1–6, 2020.

ACM/IEEE International Conference on Human Robot Interaction (HRI)

• Leigh Levinson, Manuel Dietrich, Alan Sarkisian, Selma Šabanović, and William D Smart. Effective Engineering, Stakeholder Involvement, and Regulatory Plurality Within Privacy-Aware Robotics. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 1443–1447, 2025.
• Waki Kamino, Selma Šabanović, and Malte F Jung. “A Robot's Life is Over When People Give Up”: Socio-Technical Infrastructure for Sustaining Consumer Robots. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 142–151, 2025.
• Janet Kim, Leigh Levinson, Jonathan Ota, Selma Šabanović, and William D Smart. Privacy-Sensitive Robotics: Perceptions, Measures, and Metrics. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 1976–1978, 2025.

• Long-Jing Hsu, Philip B. Stafford, Weslie Khoo, Manasi Swaminathan, Kyrie Jig Amon, Hiroki Sato, Kate Tsui, David J. Crandall, and Selma Sabanovic. “Give it time:” Longitudinal Panels Scaffold Older Adults' Learning and Robot Co-Design. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 2024.
• Waki Kamino, Malte F Jung, and Selma Sabanović. Constructing a social life with robots: Shifting away from design patterns towards interaction ritual chains. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 343–351, 2024.
• Sawyer Collins, Kenna Baugus Henkel, Zachary Henkel, Casey C Bennett, Cedomir Stanojevic, Jennifer A Piatt, Cindy L Bethel, and Selma Sabanović. " An Emotional Support Animal, Without the Animal": Design Guidelines for a Social Robot to Address Symptoms of Depression. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 147–156, 2024.
• Leigh Levinson, Christena Nippert-Eng, Randy Gomez, and Selma Sabanović. Snitches get unplugged: Adolescents' privacy concerns about robots in the home are relationally situated. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 423–432, 2024.

• Natasha Randall and Selma Sabanovic. A picture might be worth a thousand words, but it's not always enough to evaluate robots. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 437–445, 2023.
• Waki Kamino and Selma Sabanovic. Coffee, tea, robots? the performative staging of service robots in'robot cafes' in japan. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 183–191, 2023.

• Vicky Charisi, Selma Sabanović, Angelo Cangelosi, and Randy Gomez. Designing and Developing Better Robots for Children: A Fundamental Human Rights Perspective. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 712–714, 2021.

• Selma Šabanović. We're in This Together: Social Robots in Group, Organizational, and Community Interactions. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 3–4, 2020.
• Chaolan Lin, Karl F MacDorman, Selma Šabanović, Andrew D Miller, and Erin Brady. Parental expectations, concerns, and acceptance of storytelling robots for children. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 346–348, 2020.
• Megan Strait, Florian Lier, Jasmin Bernotat, Sven Wachsmuth, Friederike Eyssel, Robert Goldstone, and Selma Šabanović. A three-site reproduction of the joint simon effect with the nao robot. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 103–111, 2020.

ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports

• Waki Kamino, Andrea Wang, Dhruv Agarwal, Sil Hamilton, Eun Jeong Kang, Jieun Kim, Keigo Kusumegi, Pegah Moradi, Daniel Mwesigwa, Yan Tao, I-Ting Tsai, Ethan Yang, Shengqi Zhu, Shu-Jung Han, Chi-Jung Lee, Michael Joseph Sack, Tianhong Catherine Yu, Weslie Khoo, Andy Elliot Ricci, Yoyo Tsung-Yu Hou, Boyoung Kim, Selma Sabanovic, David J. Crandall, Karen Levy, and Malte F Jung. Million Eyes on the Robot Umps: The Case for Studying Sports in HRI Through Baseball. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 2025.
• Waki Kamino, Andrea W Wen-Yi, Dhruv Agarwal, Sil Hamilton, Eun Jeong Kang, Jieun Kim, Keigo Kusumegi, Pegah Moradi, Daniel Mwesigwa, Yan Tao, and others. Million Eyes on the “Robot Umps”: The Case for Studying Sports in HRI Through Baseball. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 1383–1388, 2025.

• Long-Jing Hsu, Weslie Khoo, Peter Lenon Goshomi, Philip B. Stafford, Manasi Swaminathan, Kate Tsui, David J. Crandall, and Selma Sabanovic. Is Now a Good Time? Opportune Moments for Interacting with an Ikigai Support Robot. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 2024.
• Manasi Swaminathan, Long-Jing Hsu, Min Min Thant, Kyrie Jig Amon, Anna Kim, Kate Tsui, Selma Sabanovic, David J. Crandall, and Weslie Khoo. If [YourName] can code, so can you! End-user robot programming for non-experts. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 2024.
• Leigh Levinson, Zachary Kaufman, Arinah Karim, Andrew Huang, Randy Gomez, and Selma Sabanovic. The Nose Knows: Using Thermal Imaging to Approximate Children's Engagement with Robots. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 669–673, 2024.
• Leigh Levinson, Manuel Dietrich, Alan Sarkisian, Selma Sabanovic, and William D Smart. Privacy aware robotics. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 1335–1337, 2024.
• Leigh Levinson, Tyler Barrett, Randy Gomez, and Selma Sabanović. Surveying Adult Perceptions of Privacy and Attitudes towards Social Robots in the Home. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 664–668, 2024.

• Weslie Khoo, Long-Jing Hsu, Kyrie Jig Amon, Pranav Vijay Chakilam, Wei-Chu Chen, Zachary Kaufman, Agness Lungu, Hiroki Sato, Erin Seliger, Manasi Swaminathan, Katherine Tsui, David Crandall, and Selma Sabanovic. Spill the Tea: When Robot Conversation Agents Support Well-being for Older Adults. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 2023.
• Daniella DiPaola, Vicky Charisi, Cynthia Breazeal, and Selma Sabanovic. Children's fundamental rights in human-robot interaction research: a systematic review. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 561–566, 2023.
• Sawyer Collins, Daniel Hicks, Zachary Henkel, Kenna Baugus Henkel, Jennifer A Piatt, Cindy L Bethel, and Selma Sabanovic. What skin is your robot in? In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 511–515, 2023.
• Natasha Randall, Waki Kamino, Arinah Karim, Wei-Chu Chen, Long-Jing Hsu, Katherine M Tsui, and Selma Sabanovic. 'Ikigai'Robots: Designing for Direct Benefits to Older Adults and Indirect Benefits to Caregivers. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 701–705, 2023.
• Houda Elmimouni, Amy Kinney, Elizabeth C Brooks, Hannah Li, and Selma Sabanovic. " Who's that?" Identity Self-Perception and Projection in the Use of Telepresence Robots in Hybrid Classrooms. In ACM/IEEE International Conference on Human Robot Interaction (HRI) Late Breaking Reports, 287–291, 2023.

ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED)

• Cheng Chu, Nai-Hui Chia, Lei Jiang, and Fan Chen. QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates. In ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2022.
• Cheng Chu, Nai-Hui Chia, Lei Jiang, and Fan Chen. Canopy: A CNFET-based Process Variation Aware Systolic DNN Accelerator. In ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2022.

• Fan Chen. Puffin: an efficient DNN training accelerator for direct feedback alignment in FeFET. In ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2021.

ACM/IEEE Joint Conference on Digital Libraries

• Allen Riddell. Reliable editions from unreliable components: estimating ebooks from print editions using profile hidden markov models. In ACM/IEEE Joint Conference on Digital Libraries, 1–5, 2022.

Advances in Neural Information Processing Systems (NeurIPS)

• Saber Sheybani, Himanshu Hansaria, Justin Wood, Linda Smith, and Zoran Tiganj. Curriculum Learning With Infant Egocentric Videos. In Advances in Neural Information Processing Systems (NeurIPS), volume 36, 54199–54212, 2023.
• Lalit Pandey, Samantha Wood, and Justin Wood. Are vision transformers more data hungry than newborn visual systems? In Advances in Neural Information Processing Systems (NeurIPS), volume 36, 73104–73121, 2023.

• Changlong Wu, Mohsen Heidari, Ananth Grama, and Wojciech Szpankowski. Expected Worst Case Regret via Stochastic Sequential Covering. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
• Chris Junchi Li, Dongruo Zhou, Quanquan Gu, and Michael Jordan. Learning two-player markov games: Neural function approximation and correlated equilibrium. In Advances in Neural Information Processing Systems (NeurIPS), volume 35, 33262–33274, 2022.
• Dongruo Zhou and Quanquan Gu. Computationally efficient horizon-free reinforcement learning for linear mixture mdps. In Advances in Neural Information Processing Systems (NeurIPS), volume 35, 36337–36349, 2022.
• Jiafan He, Dongruo Zhou, Tong Zhang, and Quanquan Gu. Nearly optimal algorithms for linear contextual bandits with adversarial corruptions. In Advances in Neural Information Processing Systems (NeurIPS), volume 35, 34614–34625, 2022.

• Brandon G. Jacques, Zoran Tiganj, Marc W. Howard, and Per B. Sederberg. DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales. In Advances in Neural Information Processing Systems (NeurIPS), 27530–27541, 2021.
• Man Shun Ang, Jianzhu Ma, Nianjun Liu, Kun Huang, and Yijie Wang. Fast projection onto the capped simplex with applications to sparse regression in bioinformatics. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
• Sadamori Kojaku, Jisung Yoon, Isabel Constantino, and Yong-Yeol Ahn. Residual2Vec: Debiasing graph embedding with random graphs. In Advances in Neural Information Processing Systems (NeurIPS), volume 34, 24150–24163, 2021.
• Weitong Zhang, Dongruo Zhou, and Quanquan Gu. Reward-free model-based reinforcement learning with linear function approximation. In Advances in Neural Information Processing Systems (NeurIPS), volume 34, 1582–1593, 2021.

• Nikolai Karpov and Qin Zhang. Batched coarse ranking in multi-armed bandits. In Advances in Neural Information Processing Systems (NeurIPS), 2020.

Annual CAD Conference

• Gabriele Guidi, Giandomenico Caruso, Laura Micoli, and others. A Method for the Automatic Generation of 3D Models based on Artificial Intelligence. In Annual CAD Conference, 149–153, 2023.

Annual Conference of the Cognitive Science Society (CogSci)

• Eden Forbes and Randall Beer. Minimal Modeling for Cognitive Ecologists: Measuring Decision-Making Trade-Offs in Ecological Tasks. In Annual Conference of the Cognitive Science Society (CogSci), volume 46, 2024.

• Jane Yang, Linda Smith, David J. Crandall, and Chen Yu. Using manual actions to create visual saliency: an outside-in solution to sustained attention and joint attention. In Annual Conference of the Cognitive Science Society (CogSci), 2023.
• Joshua McGraw, Donsuk Lee, and Justin N Wood. Parallel development of social preferences in fish and machines. In Annual Conference of the Cognitive Science Society (CogSci), volume 45, 2023.
• Denizhan Pak, Donsuk Lee, Samantha Marie Waters Wood, and Justin N Wood. A newborn embodied Turing test for view-invariant object recognition. In Annual Conference of the Cognitive Science Society (CogSci), volume 45, 2023.

• Sahaj Singh Maini, Louis Francis Labuzienski, Saurabh Gulati, and Zoran Tiganj. Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency. In Annual Conference of the Cognitive Science Society (CogSci), volume 44, 2022.
• Eeshan Hasan and Jennifer Trueblood. Representational Smoothing to Improve Medical Image Decision Making. In Annual Conference of the Cognitive Science Society (CogSci), volume 44, 2022.

• Yayun Zhang, Andrei Amatuni, Ellis Cain, Xizi Wang, David J. Crandall, and Chen Yu. Statistical learning of verb meaning. In Annual Conference of the Cognitive Science Society (CogSci), 2021.
• Ryan Peters, Andrei Amatuni, Sara Schroer, Shujon Naha, David J. Crandall, and Chen Yu. Are you with me? Modeling joint attention from egocentric vision. In Annual Conference of the Cognitive Science Society (CogSci), 2021.
• Andrei Amatuni, Sara Schroer, Ryan Peters, Md Alimoor Reza, Yayun Zhang, David J. Crandall, and Chen Yu. In-the-Moment Visual Information Determines Learning. In Annual Conference of the Cognitive Science Society (CogSci), 2021.
• Zoran Tiganj, Wei Tang, and Marc Howard. A computational model for simulating the future using a memory timeline. In Annual Conference of the Cognitive Science Society (CogSci), volume 43, 2021.
• Samantha MW Wood and Justin N Wood. Distorting Face Representations in Newborn Brains. In Annual Conference of the Cognitive Science Society (CogSci), 2021.

• Satoshi Tsutsui, Arjun Chandrasekaran, Md Alimoor Reza, David J. Crandall, and Chen Yu. A Computational Model of Early Word Learning from the Infant's Point of View. In Annual Conference of the Cognitive Science Society (CogSci), 2020.
• Justin N Wood, Donsuk Lee, Brian Wood, and Samantha MW Wood. Reverse engineering the origins of visual intelligence. In Annual Conference of the Cognitive Science Society (CogSci), 2020.

• Elizabeth M Clerkin and Linda B Smith. The everyday statistics of objects and their names: How word learning gets its start. In Annual Conference of the Cognitive Science Society (CogSci), volume 2019, 240, 2019.

Annual Meeting of the Association for Computational Linguistics (ACL)

• Tuc Nguyen and Thai Le. Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning. In Annual Meeting of the Association for Computational Linguistics (ACL), 12956–12973, 2024.
• Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, and Dongwon Lee. A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts. In Annual Meeting of the Association for Computational Linguistics (ACL), 6608–6625, 2024.

• Thai Le, Noseong Park, and Dongwon Lee. SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher. In Annual Meeting of the Association for Computational Linguistics (ACL), 6661–6674, 2022.
• Thai Le, Jooyoung Lee, Kevin Yen, Yifan Hu, and Dongwon Lee. Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense. In Annual Meeting of the Association for Computational Linguistics (ACL), 2953–2965, 2022.

Asia and South Pacific Design Automation Conference (ASP-DAC)

• Ruhan Wang, Fahiz Baba-Yara, and Fan Chen. JustQ: Automated deployment of fair and accurate quantum neural networks. In Asia and South Pacific Design Automation Conference (ASP-DAC), 121–126, 2024.

British Machine Vision Conference (BMVC)

• Shujon Naha, Md Alimoor Reza, Chen Yu, and David J. Crandall. Localizing novel attended objects in egocentric views. In British Machine Vision Conference (BMVC), 2020.

Computational Theory of Mind for Human-Machine Teams

• Taher Rahgooy, K Brent Venable, and Jennifer S Trueblood. Integrating Machine Learning and Cognitive Modeling of Decision Making. In Computational Theory of Mind for Human-Machine Teams, 173–193, 2023.

Conference on Artificial Life (ALIFE)

• Connor McShaffrey and Randall D Beer. Dissecting Viability in Multi-Agent Systems. In Conference on Artificial Life (ALIFE), volume 2024, 24, 2024.
• Randall D Beer, Connor McShaffrey, and Thomas M Gaul. Deriving the intrinsic viability constraint of an emergent individual from first principles. In Conference on Artificial Life (ALIFE), volume 2024, 23, 2024.
• Eden Forbes and Randall Beer. Deriving Community Models with Evolutionary Robotics: A Case Study of Sensory Pollution. In Conference on Artificial Life (ALIFE), volume 2024, 22, 2024.
• Denizhan Pak and Randall D Beer. Comparative Analysis of Heteroclinic Network Construction Algorithms for Simulating C. elegans Behavioral and Neural Data. In Conference on Artificial Life (ALIFE), volume 2024, 18, 2024.

• Connor McShaffrey and Randall D Beer. Decomposing viability space. In Conference on Artificial Life (ALIFE), volume 2023, 51, 2023.
• Lindsay Stolting, Randall D Beer, and Eduardo J Izquierdo. Characterizing the Role of Homeostatic Plasticity in Central Pattern Generators. In Conference on Artificial Life (ALIFE), volume 2023, 92, 2023.

• Eduardo J Izquierdo, Gabriel J Severino, and Haily Merritt. Perpetual Crossers without Sensory Delay: Revisiting the Perceptual Crossing Simulation Studies. In Conference on Artificial Life (ALIFE), 2022.
• Eduardo J Izquierdo and Madhavun Candadai. What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit? In Conference on Artificial Life (ALIFE), 2022.

• Abe Leite and Eduardo J Izquierdo. Generating reward structures on a parameterized distribution of dynamics tasks. In Conference on Artificial Life (ALIFE), number 2021, 2021.

• Abe Leite, Madhavun Candadai, and Eduardo J Izquierdo. Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution. In Conference on Artificial Life (ALIFE), 441–449, 2020.
• Brian A Dahlberg and Eduardo J Izquierdo. Contributions from parallel strategies for spatial orientation in C. elegans. In Conference on Artificial Life (ALIFE), 16–24, 2020.
• Mahi Luthra, Eduardo J Izquierdo, and Peter M Todd. Cognition evolves with the emergence of environmental patchiness. In Conference on Artificial Life (ALIFE), 2020.
• Graham Todd, Madhavun Candadai, and Eduardo J Izquierdo. Interaction between evolution and learning in nk fitness landscapes. In Conference on Artificial Life (ALIFE), 761–767, 2020.
• Lauren V Benson, Madhavun Candadai, and Eduardo J Izquierdo. Neural reuse in multifunctional neural networks for control tasks. In Conference on Artificial Life (ALIFE), number 32, 2020.
• Randall D Beer. An investigation into the origin of autopoiesis. In Conference on Artificial Life (ALIFE), volume 26, 5–22, 2020.
• Randall D Beer. An integrated perspective on the constitutive and interactive dimensions of autonomy. In Conference on Artificial Life (ALIFE), 202–209, 2020.

Conference on Empirical Methods in Natural Language Processing (EMNLP)

• Tuc Nguyen, Yifan Hu, and Thai Le. Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Bang Trinh Tran To and Thai Le. Harry potter is still here! probing knowledge leakage in targeted unlearned large language models via automated adversarial prompting. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Binh Nguyen, Shuji Shi, Ryan Ofman, and Thai Le. What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.

• Tuc Nguyen and Thai Le. Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 21183–21203, 2024.

• Christopher Burger, Lingwei Chen, and Thai Le. “Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 12831–12844, 2023.
• Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, and others. MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 9960–9987, 2023.
• Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, and others. STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 9304–9315, 2023.

• Bo Feng, Qian Lou, Lei Jiang, and Geoffrey Fox. CRYPTOGRU: Low Latency Privacy-Preserving Text Model Inference. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

• Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. Authorship attribution for neural text generation. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 8384–8395, 2020.

Conference on Information and Knowledge Management (CIKM)

• Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, and Xiaozhong Liu. Community-based hierarchical positive-unlabeled (pu) model fusion for chronic disease prediction. In Conference on Information and Knowledge Management (CIKM), 2747–2756, 2023.

• Yi Li, Yan Song, and Qin Zhang. Learning to cluster via same-cluster queries. In Conference on Information and Knowledge Management (CIKM), 978–987, 2021.

• Mohsen Sayyadiharikandeh, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. Detection of novel social bots by ensembles of specialized classifiers. In Conference on Information and Knowledge Management (CIKM), 2725–2732, 2020.

Conference on Learning Theory (COLT)

• Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, and Quanquan Gu. Variance-dependent regret bounds for linear bandits and reinforcement learning: Adaptivity and computational efficiency. In Conference on Learning Theory (COLT), 4977–5020, 2023.

• Dongruo Zhou, Quanquan Gu, and Csaba Szepesvari. Nearly minimax optimal reinforcement learning for linear mixture markov decision processes. In Conference on Learning Theory (COLT), 4532–4576, 2021.

Conference on Recent Advances in NLP (RANLP)

• Santiago Arróniz and Sandra Kübler. Was That a Question? Automatic Classification of Discourse Meaning in Spanish. In Conference on Recent Advances in NLP (RANLP), 132–142, 2023.

• He Zhou and Sandra Kübler. Delexicalized Cross-lingual Dependency Parsing for Xibe. In Conference on Recent Advances in NLP (RANLP), 2021.
• Holly Lopez Long, Alexandra O'Neill, and Sandra Kübler. On the Interaction between Annotation Quality and Classifier Performance in Abusive Language Detection. In Conference on Recent Advances in NLP (RANLP), 2021.
• Keith Carlson, Allen Riddell, and Daniel Rockmore. Unsupervised text style transfer with content embeddings. In Conference on Recent Advances in NLP (RANLP), 226–233, 2021.
• Allen Riddell and Yohei Igarashi. Varieties of Plain Language. In Conference on Recent Advances in NLP (RANLP), 1180–1187, 2021.
• Almas Abdibayev, Allen Riddell, and Daniel Rockmore. BPoMP: The Benchmark of Poetic Minimal Pairs–Limericks, Rhyme, and Narrative Coherence. In Conference on Recent Advances in NLP (RANLP), 1–9, 2021.
• Allen Riddell, Haining Wang, and Patrick Juola. A call for clarity in contemporary authorship attribution evaluation. In Conference on Recent Advances in NLP (RANLP), 2021.

Conference on Robot Learning (CORL)

• Youwei Yu, Junhong Xu, and Lantao Liu. Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation. In Conference on Robot Learning (CORL), 864–884, 2025.

Conference on Uncertainty in Artificial Intelligence (UAI)

• Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, and Dongruo Zhou. Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation. In Conference on Uncertainty in Artificial Intelligence (UAI), 2025.

• Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, and Quanquan Gu. Provably efficient representation selection in low-rank markov decision processes: from online to offline rl. In Conference on Uncertainty in Artificial Intelligence (UAI), 2488–2497, 2023.

Design, Automation \& Test in Europe Conference \& Exhibition (DATE)

• Fan Chen, Linghao Song, Hai Li, and Yiran Chen. MARVEL: A Vertical Resistive Accelerator for Low-Power Deep Learning Inference in Monolithic 3D. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021.
• Fan Chen, Linghao Song, Hai Li, and Yiran Chen. RAISE: A Resistive Accelerator for Subject-Independent EEG Signal Classification. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021.

• Yitu Wang, Fan Chen, Linghao Song, C-J Richard Shi, Hai Helen Li, and Yiran Chen. Reboc: Accelerating block-circulant neural networks in reram. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020.

EAI International Conference on AI for People, Democratizing AI

• Sadia Khan, Alfonso Morales, and Beth Plale. Democratization is a Process, not a Destination: Operationalizing Ethics and Democratization in a Cyberinfrastructure for AI Project. In EAI International Conference on AI for People, Democratizing AI, 29–45, 2023.

Educational Data Mining

• Halim Acosta, Seung Lee, Bradford Mott, Haesol Bae, Krista Glazewski, Cindy Hmelo-Silver, and James Lester. Multimodal learning analytics for predicting student collaboration satisfaction in collaborative game-based learning. In Educational Data Mining, 2024.

European Chapter of the Association for Computational Linguistics

• Haining Wang, Allen Riddell, and Patrick Juola. Mode effects' challenge to authorship attribution. In European Chapter of the Association for Computational Linguistics, 1146–1155, 2021.

European Conference on Artificial Intelligence (ECAI)

• Adaku Uchendu, Thai Le, and Dongwon Lee. TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles. In European Conference on Artificial Intelligence (ECAI), 1446–1454, 2024.

European Conference on Computer Vision (ECCV)

• Mang Ye, Jianbing Shen, David J. Crandall, Ling Shao, and Jiebo Luo. Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification. In European Conference on Computer Vision (ECCV), 2020.

European Conference on IR Research (ECIR)

• Dominic Seyler, Wei Liu, XiaoFeng Wang, and ChengXiang Zhai. Towards dark jargon interpretation in underground forums. In European Conference on IR Research (ECIR), 393–400, 2021.

European Signal Processing Conference (EUSIPCO)

• R David Badger, Kristopher H Jung, and Minje Kim. An Open-Sourced Time-Frequency Domain RF Classification Framework. In European Signal Processing Conference (EUSIPCO), 1701–1705, 2021.
• R David Badger and Minje Kim. Singular Value Decomposition for Compression of Large-Scale Radio Frequency Signals. In European Signal Processing Conference (EUSIPCO), 1591–1595, 2021.

Findings of the Association for Computational Linguistics

• Dang Cuong, Dung Le, and Thai Le. A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models. In Findings of the Association for Computational Linguistics, 13475–13491, 2024.

• Ziyao Wang, Thai Le, and Dongwon Lee. UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning. In Findings of the Association for Computational Linguistics, 11952–11965, 2023.
• Nafis Irtiza Tripto, Adaku Uchendu, Thai Le, Mattia Setzu, Fosca Giannotti, and Dongwon Lee. HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis. In Findings of the Association for Computational Linguistics, 13706–13724, 2023.

• Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee. TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation. In Findings of the Association for Computational Linguistics, 2001–2016, 2021.
• Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, and Yong-Yeol Ahn. Predicting anti-Asian hateful users on Twitter during COVID-19. In Findings of the Association for Computational Linguistics, 2021.

• Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, and Lawrence Moss. OCNLI: Original Chinese Natural Language Inference. In Findings of the Association for Computational Linguistics, 2020.

IEEE Conference on Artificial Intelligence (CAI)

• Juliette Zerick, Zachary Kaufman, Jonathan Ott, Janki Kuber, Ember Chow, Shyama Shah, and Gregory Lewis. It Takes Two to Trust: Mediating Human-AI Trust for Resilience and Reliability. In IEEE Conference on Artificial Intelligence (CAI), 755–761, 2024.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

• Xizi Wang, Feng Cheng, and Gedas Bertasius. LoCoNet: Long-Short Context Network for Active Speaker Detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
• Ziwei Zhao, Yuchen Wang, and Chuhua Wang. Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
• Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C.V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, and Michael Wray. Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
• Shreyas Fadnavis, Agniva Chowdhury, Joshua Batson, Petros Drineas, and Eleftherios Garyfallidis. Patch2self2: Self-supervised denoising on coresets via matrix sketching. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27641–27651, 2024.

• Feng Cheng, Xizi Wang, Jie Lei, David J. Crandall, Mohit Bansal, and Gedas Bertasius. VindLU: A recipe for Effective Video-and-Language Pretraining. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
• Mengxin Zheng, Qian Lou, and Lei Jiang. Trojvit: Trojan Insertion in Vision Transformers. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4025–4034, 2023.

• Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David J. Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4D: Around the World in 3,000 Hours of Egocentric Video. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

• Norman Su and David J. Crandall. The Affective Growth of Computer Vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

• Bardia Doosti, Shujon Naha, Majid Mirbagheri, and David J. Crandall. HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
• Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David J. Crandall, and Steven Hoi. Learning Video Object Segmentation from Unlabeled Videos. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

IEEE Conference on Robotics and Automation (ICRA)

• Abe Leininger, Mahmoud Ali, Hassan Jardali, and Lantao Liu. Gaussian process-based traversability analysis for terrain mapless navigation. In IEEE Conference on Robotics and Automation (ICRA), 10925–10931, 2024.
• Hassan Jardali, Mahmoud Ali, and Lantao Liu. Autonomous mapless navigation on uneven terrains. In IEEE Conference on Robotics and Automation (ICRA), 13227–13233, 2024.
• Weizheng Wang, Le Mao, Ruiqi Wang, and Byung-Cheol Min. Multi-robot cooperative socially-aware navigation using multi-agent reinforcement learning. In IEEE Conference on Robotics and Automation (ICRA), 12353–12360, 2024.

• Junhong Xu, Kai Yin, Jason M Gregory, and Lantao Liu. Causal Inference for De-biasing Motion Estimation from Robotic Observational Data. In IEEE Conference on Robotics and Automation (ICRA), 2023.
• Mahmoud Ali and Lantao Liu. Light-Weight Pointcloud Representation with Sparse Gaussian Process. In IEEE Conference on Robotics and Automation (ICRA), 4931–4937, 2023.
• Runsheng Xu, Weizhe Chen, Hao Xiang, Xin Xia, Lanao Liu, and Jiaqi Ma. Model-Agnostic Multi-Agent Perception Framework. In IEEE Conference on Robotics and Automation (ICRA), 2023.

• Weizhe Chen and Lantao Liu. Informative Planning in the Presence of Outliers. In IEEE Conference on Robotics and Automation (ICRA), 2022.

• Zehua Zhang, Ashish Tawari, Sujitha Martin, and David J. Crandall. Interaction Graph for Object Importance Estimation in On-road Driving Videos. In IEEE Conference on Robotics and Automation (ICRA), 2020.

IEEE European Symposium on Security and Privacy (EuroS\&P)

• Yunhui Long, Lei Wang, Diyue Bu, Vincent Bindschaedler, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. A pragmatic approach to membership inferences on machine learning models. In IEEE European Symposium on Security and Privacy (EuroS&P), 521–534, 2020.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

• Cheng Chu, Aishwarya Hastak, and Fan Chen. LSTM-QGAN: Scalable NISQ Generative Adversarial Network. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5, 2025.

• Hao Zhang, Srivatsan Kandadai, Harsha Rao, Minje Kim, Tarun Pruthi, and Trausti Kristjansson. Deep Adaptive Aec: Hybrid of Deep Learning and Adaptive Acoustic Echo Cancellation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 756–760, 2022.
• Darius Petermann and Minje Kim. Spain-Net: Spatially-Informed Stereophonic Music Source Separation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 106–110, 2022.
• Sunwoo Kim and Minje Kim. Bloom-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 366–370, 2022.
• Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, and Wontak Kim. Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 426–430, 2022.

• Haici Yang, Kai Zhen, Seungkwon Beack, and Minje Kim. Source-aware neural speech coding for noisy speech compression. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 706–710, 2021.

• Sunwoo Kim, Haici Yang, and Minje Kim. Boosted locality sensitive hashing: Discriminative binary codes for source separation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 106–110, 2020.
• Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, and Minje Kim. Efficient and scalable neural residual waveform coding with collaborative quantization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 361–365, 2020.
• Sanna Wager, George Tzanetakis, Cheng-i Wang, and Minje Kim. Deep autotuner: A pitch correcting network for singing performances. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 246–250, 2020.

IEEE International Conference on Big Data (BIGDATA)

• Harlin Lee, Rishi Sonthalia, and Jacob G Foster. Dynamic embedding-based methods for link prediction in machine learning semantic network. In IEEE International Conference on Big Data (BIGDATA), 5801–5808, 2021.

IEEE International Conference on Cluster Computing (CLUSTER)

• Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, and Franck Cappello. Exploring autoencoder-based error-bounded compression for scientific data. In IEEE International Conference on Cluster Computing (CLUSTER), 2021.

IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)

• Parichit Sharma, Hasan Kurban, Mehmet Dalkilic, and Mustafa Kurban. Instance-Based Learning-Driven Density of States Analysis in Functionalized Fullerene Derivatives for Optimizing Organic Photovoltaics. In IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), 1–6, 2025.

IEEE International Conference on Computer Vision (ICCV)

• Juhyung Ha, Vibhas Kumar Vats, Alimoor Reza, Soon-heung Jung, and David J. Crandall. HVPUNet: Hybrid-Voxel Point-cloud Upsampling Network. In IEEE International Conference on Computer Vision (ICCV), 2025.
• Chi-Hsi Kung, Frangil M Ramirez, Juhyung Ha, Yi-Hsuan Tsai, Yi-Ting Chen, and David J. Crandall. What Changed and What Could Have Changed? State-Change Counterfactuals for Procedure-Aware Video Representation Learning. In IEEE International Conference on Computer Vision (ICCV), 2025.

IEEE International Conference on Data Engineering (ICDE)

• Thai Le, Yiran Ye, Yifan Hu, and Dongwon Lee. Cryptext: Database and interactive toolkit of human-written text perturbations in the wild. In IEEE International Conference on Data Engineering (ICDE), 3639–3642, 2023.

IEEE International Conference on Data Mining (ICDM)

• Thai Le, Suhang Wang, and Dongwon Lee. Malcom: Generating malicious comments to attack neural fake news detection models. In IEEE International Conference on Data Mining (ICDM), 282–291, 2020.
• Md Khaledur Rahman, Majedul Haque Sujon, and Ariful Azad. Force2Vec: Parallel force-directed graph embedding. In IEEE International Conference on Data Mining (ICDM), 442–451, 2020.

IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

• Satoshi Tsutsui, Xizi Wang, Guangyuan Weng, Yayun Zhang, David J. Crandall, and Chen Yu. Action Recognition based on Cross-Situational Action-object Statistics. In IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2022.

IEEE International Conference on Intelligence and Security Informatics (ISI)

• Adhishree Kathikar, Aishwarya Nair, Ben Lazarine, Agrim Sachdeva, and Sagar Samtani. Assessing the vulnerabilities of the open-source artificial intelligence (ai) landscape: A large-scale analysis of the hugging face platform. In IEEE International Conference on Intelligence and Security Informatics (ISI), 1–6, 2023.

• Tala Vahedi, Benjamin Ampel, Sagar Samtani, and Hsinchun Chen. Identifying and categorizing malicious content on paste sites: a neural topic modeling approach. In IEEE International Conference on Intelligence and Security Informatics (ISI), 1–6, 2021.

• Benjamin Ampel, Sagar Samtani, Hongyi Zhu, Steven Ullman, and Hsinchun Chen. Labeling hacker exploits for proactive cyber threat intelligence: a deep transfer learning approach. In IEEE International Conference on Intelligence and Security Informatics (ISI), 1–6, 2020.

IEEE International Conference on Intelligent Robots and Systems (IROS)

• Zheng Chen, Deepak Duggirala, David J. Crandall, Lei Jiang, and Lantao Liu. SePaint: Semantic Map Inpainting via Multinomial Diffusion. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2024.
• Durgakant Pushp, Junhong Xu, Zheng Chen, and Lantao Liu. Context-Generative Default Policy for Bounded Rational Agent. In IEEE International Conference on Intelligent Robots and Systems (IROS), 7703–7709, 2024.
• Mahmoud Ali, Durgkant Pushp, Zheng Chen, and Lantao Liu. Visual-Geometry GP-based Navigable Space for Autonomous Navigation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 10066–10072, 2024.
• Shyam Sundar Kannan, Vishnunandan LN Venkatesh, and Byung-Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models. In IEEE International Conference on Intelligent Robots and Systems (IROS), 12140–12147, 2024.
• Vishnunandan LN Venkatesh and Byung-Cheol Min. Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods. In IEEE International Conference on Intelligent Robots and Systems (IROS), 10754–10761, 2024.
• Taehyeon Kim and Byung-Cheol Min. Semantic Layering in Room Segmentation via LLMs. In IEEE International Conference on Intelligent Robots and Systems (IROS), 9831–9838, 2024.

• Imran Kabir, Shaurya Shubham, Vijayalaxmi Bhimrao Maigur, Mahesh Ravindra Latnekar, Mayank Kumar Raunak, Nikhil Shripad Thakurdesai, David J. Crandall, and Md Reza. Few-shot segmentation and Semantic Segmentation for Underwater Imagery. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2023.
• Zheng Chen, Zhengming Ding, David Crandall, and Lantao Liu. Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2023.
• Durgakant Pushp, Junhong Xu, and Lantao Liu. Coordination of bounded rational drones through informed prior policy. In IEEE International Conference on Intelligent Robots and Systems (IROS), 6779–6786, 2023.
• Zheng Chen, Zhengming Ding, Jason M Gregory, and Lantao Liu. Ida: Informed domain adaptive semantic segmentation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 90–97, 2023.
• Ihab S Mohamed, Mahmoud Ali, and Lantao Liu. GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2023.
• Go-Eum Cha, Wonse Jo, and Byung-Cheol Min. Implications of personality on cognitive workload, affect, and task performance in remote robot control. In IEEE International Conference on Intelligent Robots and Systems (IROS), 4153–4160, 2023.
• Weizheng Wang, Ruiqi Wang, Le Mao, and Byung-Cheol Min. Navistar: Socially aware robot navigation with hybrid spatio-temporal graph transformer and preference learning. In IEEE International Conference on Intelligent Robots and Systems (IROS), 11348–11355, 2023.
• Shyam Sundar Kannan, Vishnunandan LN Venkatesh, Revanth Krishna Senthilkumaran, and Byung-Cheol Min. Upplied: Uav path planning for inspection through demonstration. In IEEE International Conference on Intelligent Robots and Systems (IROS), 1126–1133, 2023.
• Ruiqi Wang, Dezhong Zhao, and Byung-Cheol Min. Initial task allocation for multi-human multi-robot teams with attention-based deep reinforcement learning. In IEEE International Conference on Intelligent Robots and Systems (IROS), 7915–7922, 2023.
• Tamzidul Mina, Wonse Jo, Shyam S Kannan, and Byung-Cheol Min. Beacon-based Distributed Structure Formation in Multi-agent Systems. In IEEE International Conference on Intelligent Robots and Systems (IROS), 9277–9284, 2023.

• Eric Nichols, Sarah Rose Siskind, Levko Ivanchuk, Guillermo Pérez, Waki Kamino, Selma Šabanović, and Randy Gomez. Hey Haru, Let's Be Friends! Using the Tiers of Friendship to Build Rapport through Small Talk with the Tabletop Robot Haru. In IEEE International Conference on Intelligent Robots and Systems (IROS), volume, 6101–6108, 2022.
• Durgakant Pushp, Swapnil Kalhapure, Kaushik Das, and Lantao Liu. UAV-miniUGV Hybrid System for Hidden Area Exploration and Manipulation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 1297–1304, 2022.
• Ruiqi Wang, Weizheng Wang, and Byung-Cheol Min. Feedback-efficient active preference learning for socially aware robot navigation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 11336–11343, 2022.

• Jagpreet Chawla, Nikhil Shripad Thakurdesai, Anuj Balasaheb Godase, Md Alimoor Reza, David J. Crandall, and Soon-Heung Jung. Error Diagnosis of Deep Monocular Depth Estimation Models. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2021.

IEEE International Conference on Multimedia and Expo (ICME)

• Yuchen Wang, Mingze Xu, John Paden, Lara Koenig, Geoffrey C. Fox, and David J. Crandall. Deep Tiered Image Segmentation for Detecting Internal Ice Layers in Radar Imagery. In IEEE International Conference on Multimedia and Expo (ICME), 2021.

IEEE International Conference on Quantum Computing and Engineering (QCE)

• Min Yang, Xiaolong Guo, and Lei Jiang. Multi-stage watermarking for quantum circuits. In IEEE International Conference on Quantum Computing and Engineering (QCE), volume 1, 796–804, 2024.
• Chi Zhang, Akriti Kumari, and Damir Cavar. Entangled Meanings: Classification and Ambiguity Resolution in QNLP. In IEEE International Conference on Quantum Computing and Engineering (QCE), volume 2, 97–102, 2024.

IEEE International Conference on Robot \& Human Interactive Communication (RO-MAN)

• Long-Jing Hsu, Weslie Khoo, Manasi Swaminathan, Kyrie Jig Amon, Rasika Muralidharan, Hiroki Sato, Min Min Thant, Anna S. Kim, Katherine M Tsui, David J Crandall, and Selma Šabanovic. Let's Talk About You: Development and Evaluation of an Autonomous Robot to Support Ikigai Reflection in Older Adults. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2024.
• Leigh Levinson, Randy Gomez, and Selma Šabanović. Bow ties & colorful eyes: Centering youth designs of social robots. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1667–1674, 2024.
• Sawyer Collins, Marlena R Fraune, Kyrie Jig Amon, Eliot R Smith, and Selma Šabanović. Ties That Bind: Group Effects in Human-Robot Team Interaction in Japan and the United States. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1503–1509, 2024.
• Sawyer Collins, Zachary Henkel, Kenna Baugus Henkel, Casey C Bennett, Čedomir Stanojević, Jennifer A Piatt, Cindy L Bethel, and Selma Šabanović. The Ins and Outs of Socially Assistive Robots: Sensors and Behaviors of a Therapeutic Robot for Depression Management. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1624–1629, 2024.
• Waki Kamino, Selma Šabanović, and Malte F Jung. The lifecycle of social robots: Obsolescence and values in repair. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2147–2154, 2024.
• Long-Jing Hsu, Weslie Khoo, Manasi Swaminathan, Kyrie Jig Amon, Rasika Muralidharan, Hiroki Satov, Min Min Thant, Anna S Kim, Katherine M Tsui, David J Crandall, and others. Let’s talk about you: Development and evaluation of an autonomous robot to support Ikigai reflection in older adults. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1323–1330, 2024.

• Long-Jing Hsu, Weslie Khoo, Natasha Randall, Waki Kamino, Swapna Joshi, Hiroki Sato, David J. Crandall, Kate Tsui, and Selma Sabanovic. Finding Its Voice: Exploring the Influence of Robot Voices on Fit, Social Attributes, and Willingness Among Older Adults in the U.S. and Japan. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2023.
• Gonzalo A García, Guillermo Pérez, Leigh Levinson, J Gabriel Amores, Gloria Alvarez-Benito, Manuel Castro-Malet, Mario Castaño-Ocaña, Marta J López-González de Quevedo, Ricardo Durán-Viñuelas, Randy Gomez, and others. Living with Haru4Kids: Study on children’s activity and engagement in a family-robot cohabitation scenario. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1428–1435, 2023.
• Waki Kamino, Natasha Randall, Tanya Saga, Long-Jing Hsu, Katherine M Tsui, Selma Šabanović, and Shinichi Nagata. We all make mistakes: Terminal, non-critical, recoverable, and favorable interaction failures between people and a social robot. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1028–1033, 2023.
• Guillermo Pérez, Gonzalo A García, Manuel Castro, Mario Castaño, Marta J Lopez-González De Quevedo, Ricardo Durán, J Gabriel Amores, Gloria Álvarez, Leigh Levinson, Selma Šabanović, and others. Child-robot conversation in the wild wild home: A language processing user study. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1436–1442, 2023.
• Casey C Bennett, Selma Sabanovic, Cedomir Stanojevic, Zachary Henkel, Seongcheol Kim, Jinjae Lee, Kenna Baugus, Jennifer A Piatt, Janghoon Yu, Jiyeong Oh, and others. Enabling robotic pets to autonomously adapt their own behaviors to enhance therapeutic effects: a data-driven approach. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1625–1632, 2023.

• Sawyer Collins and Selma Šabanović. “What Does Your Robot Do?” A Tabletop Role-Playing Game to Support Robot Design. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 1097–1102, 2021.

• Heike Brock, Selma Sabanovic, Keisuke Nakamura, and Randy Gomez. Robust real-time hand gestural recognition for non-verbal communication with tabletop robot haru. In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 891–898, 2020.

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

• Veda Narayana Koraganji, Aidan J Whelan, Akhil Mokkapati, Juliette N Zerick, R Michael Winters, and Gregory F Lewis. Performance of 1D-CNNs for EEG-Based Mental State Classification: Effects of Domain, Window Size and Electrode Montage. In IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2664–2671, 2021.

IEEE International Conference on e-Science (e-Science)

• Sachith Withana and Beth Plale. Patra ModelCards: AI/ML Accountability in the Edge-Cloud Continuum. In IEEE International Conference on e-Science (e-Science), 1–10, 2024.

• Beth Plale, Sadia Khan, and Alfonso Morales. Democratization of AI: Challenges of AI Cyberinfrastructure and Software Research. In IEEE International Conference on e-Science (e-Science), 1–3, 2023.
• Sachith Withana and Beth Plale. CKN: An edge AI distributed framework. In IEEE International Conference on e-Science (e-Science), 1–10, 2023.

IEEE International Parallel and Distributed Processing Symposium (IPDPS)

• Isuru Ranawaka, Md Khaledur Rahman, and Ariful Azad. Distributed sparse random projection trees for constructing k-nearest neighbor graphs. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), 36–46, 2023.

• Md Khaledur Rahman, Majedul Haque Sujon, and Ariful Azad. Fusedmm: A unified sddmm-spmm kernel for graph embedding and graph neural networks. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), 256–266, 2021.

IEEE International Symposium on High Performance Computer Architecture

• Linghao Song, Fan Chen, Youwei Zhuo, Xuehai Qian, Hai Li, and Yiran Chen. Accpar: Tensor partitioning for heterogeneous deep learning accelerators. In IEEE International Symposium on High Performance Computer Architecture, 2020.

IEEE International Symposium on Information Theory (ISIT)

• Mohsen Heidari, Achilleas Anastasopoulos, and S Sandeep Pradhan. Upper Bounds on the Feedback Error Exponent of Channels With States and With Memory. In IEEE International Symposium on Information Theory (ISIT), 1330–1335, 2022.
• Changlong Wu, Mohsen Heidari, Ananth Grama, and Wojciech Szpankowski. Sequential vs. fixed design regrets in online learning. In IEEE International Symposium on Information Theory (ISIT), 438–443, 2022.

• Mohsen Heidari, Arun Padakandla, and Wojciech Szpankowski. A theoretical framework for learning from quantum data. In IEEE International Symposium on Information Theory (ISIT), 1469–1474, 2021.

IEEE International Symposium on Technologies for Homeland Security (HST)

• Md Taufique Hussain, Arif Khan, Ariful Azad, Samrat Chatterjee, Robert Brigantic, and Mahantesh Halappanavar. Disruption-robust community detection using consensus clustering in complex networks. In IEEE International Symposium on Technologies for Homeland Security (HST), 1–6, 2022.

IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)

• Casey C Bennett, Cedomir Stanojevic, Seongcheol Kim, Jinjae Lee, Janghoon Yu, Jiyeong Oh, Selma Sabanovic, and Jennifer A Piatt. Comparison of in-home robotic companion pet use in South Korea and the United States: a case study. In IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 01–07, 2022.

IEEE Security and Privacy (Oakland)

• Yi Chen, Yepeng Yao, XiaoFeng Wang, Dandan Xu, Chang Yue, Xiaozhong Liu, Kai Chen, Haixu Tang, and Baoxu Liu. Bookworm game: Automatic discovery of LTE vulnerabilities through documentation analysis. In IEEE Security and Privacy (Oakland), 1197–1214, 2021.

• Rakibul Hasan, David J. Crandall, Mario Fritz, and Apu Kapadia. Automatically Detecting Bystanders in Photos to Reduce Privacy Risks. In IEEE Security and Privacy (Oakland), 2020.

IEEE Symposium Series on Computational Intelligence (SSCI)

• Connor McShaffrey and Randall Beer. Maintaining viability with multiple needs. In IEEE Symposium Series on Computational Intelligence (SSCI), 1523–1528, 2022.

IEEE Symposium on Security and Privacy (SP)

• Zihao Wang, Rui Zhu, Zhikun Zhang, Haixu Tang, and XiaoFeng Wang. Rigging the Foundation: Manipulating Pre-training for Advanced Membership Inference Attacks. In IEEE Symposium on Security and Privacy (SP), 2509–2526, 2025.

• Rui Zhu, Di Tang, Siyuan Tang, XiaoFeng Wang, and Haixu Tang. Selective amnesia: On efficient, high-fidelity and blind suppression of backdoor effects in trojaned machine learning models. In IEEE Symposium on Security and Privacy (SP), 1–19, 2023.

IEEE Virtual Reality and 3D User Interfaces (VR)

• Seungwon Paik, Youngseung Jeon, Patrick C Shih, and Kyungsik Han. I Feel More Engaged When I Move!: Deep Learning-based Backward Movement Detection and its Application. In IEEE Virtual Reality and 3D User Interfaces (VR), 2021.

IEEE Winter Conference on Applications of Computer Vision (WACV)

• Juhyung Ha, Jong Sung Park, David J. Crandall, Eleftherios Garyfallidis, and Xuhong Zhang. Multi-resolution Guided 3D GANs for Medical Image Translation. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2025.

• Vibhas K. Vats, Sripad Joshi, David J. Crandall, Md. Alimoor Reza, and Soon-heung Jung. GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2024.
• Zheng Chen, Zhiqi Zhang, Junsong Yuan, Yi Xu, and Lantao Liu. Show Your Face: Restoring Complete Facial Images from Partial Observations for VR Meeting. In IEEE Winter Conference on Applications of Computer Vision (WACV), 8688–8697, 2024.

• Zhenhua Chen, Chuhua Wang, and David J. Crandall. Semantically Stealthy Adversarial Attacks against Segmentation Models. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022.
• Zehua Zhang and David J. Crandall. Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022.

• Satoshi Tsutsui, Yanwei Fu, and David J. Crandall. Whose hand is this? Person Identification from Egocentric Hand Gestures. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2021.
• Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, and David J. Crandall. Part Segmentation of Unseen Objects Using Keypoint Guidance. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2021.
• Tongxin Wang, Zhengming Ding, Wei Shao, Haixu Tang, and Kun Huang. Towards fair cross-domain adaptation via generative learning. In IEEE Winter Conference on Applications of Computer Vision (WACV), 454–463, 2021.

IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)

• Farzaneh Zokaee, Bing Li, and Fan Chen. FeFET-based Process-in-Memory Architecture for Low-Power DNN Training. In IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 2021.

International Conference of the Learning Sciences (ICLS)

• Srijita Chakraburty, Daria Smyslova, Cindy E Hmelo-Silver, Krista Glazewski, Oluwatomisin Obajemu, Christine Fry Wise, Toni Earle-Randell, Tom McKlin, and Kristy Elizabeth Boyer. Engaging with Natural Language Processing: An Exploratory Study across Multiple Middle School Grades. In International Conference of the Learning Sciences (ICLS), 2025.

International Conference on Advances in Social Network Analysis and Mining (ASONAM)

• Erfan Samieyan Sahneh, Gianluca Nogara, Matthew R DeVerna, Nick Liu, Luca Luceri, Filippo Menczer, Francesco Pierri, and Silvia Giordano. The Dawn of Decentralized Social Media: An Exploration of Bluesky's Public Opening. In International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2024.

International Conference on Algorithmic Learning Theory

• Zixiang Chen, Dongruo Zhou, and Quanquan Gu. Faster perturbed stochastic gradient methods for finding local minima. In International Conference on Algorithmic Learning Theory, 176–204, 2022.
• Zixiang Chen, Dongruo Zhou, and Quanquan Gu. Almost optimal algorithms for two-player zero-sum linear mixture markov games. In International Conference on Algorithmic Learning Theory, 227–261, 2022.

International Conference on Animal-Computer Interaction

• Cassie Kresnye, Sam Rasmussen, Mia Gallardo, and Patrick Shih. Payload Drones and ACI: Drone Navigation System Prototype. In International Conference on Animal-Computer Interaction, 2021.

• K Cassie Kresnye and Patrick C Shih. Movement Patterns as Enrichment: Exploratory Canine-Drone Interaction Pilot Study. In International Conference on Animal-Computer Interaction, 2020.
• K Cassie Kresnye, Christopher Flynn Martin, and Patrick C Shih. Drone Delivery Service: An Orangutan Enrichment Pilot Study. In International Conference on Animal-Computer Interaction, 2020.

International Conference on Artificial Intelligence and Statistics (AISTATS)

• Zhiyong Wang, Jize Xie, Yi Chen, John CS Lui, and Dongruo Zhou. Variance-Dependent Regret Bounds for Nonstationary Linear Bandits. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2503–2511, 2025.

• Mohsen Heidari and Wojciech Szpankowski. Agnostic PAC Learning of $ k $-juntas Using $ L_2 $-Polynomial Regression. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2922–2938, 2023.

• Yue Wu, Dongruo Zhou, and Quanquan Gu. Nearly minimax optimal regret for learning infinite-horizon average-reward mdps with linear function approximation. In International Conference on Artificial Intelligence and Statistics (AISTATS), 3883–3913, 2022.

• Dongruo Zhou and Quanquan Gu. Stochastic recursive variance-reduced cubic regularization methods. In International Conference on Artificial Intelligence and Statistics (AISTATS), 3980–3990, 2020.

International Conference on Artificial Intelligence in Education

• Robert E Cummings, Thai Le, Sijan Shrestha, and Carrie Veronica Smith. The unexpected effects of google smart compose on open-ended writing tasks. In International Conference on Artificial Intelligence in Education, 468–481, 2024.

International Conference on Artificial Intelligence in Medicine (AIME)

• Tingyi Wanyan, Martin Kang, Marcus A Badgeley, Kipp W Johnson, Jessica K De Freitas, Fayzan F Chaudhry, Akhil Vaid, Shan Zhao, Riccardo Miotto, Girish N Nadkarni, and others. Heterogeneous graph embeddings of electronic health records improve critical care disease predictions. In International Conference on Artificial Intelligence in Medicine (AIME), 14–25, 2020.

International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

• Risal Shahriar Shefin, Md Asifur Rahman, Thai Le, and Sarra Alqahtani. xSRL: Safety-Aware Explainable Reinforcement Learning-Safety as a Product of Explainability. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 1932–1940, 2025.

• Joe McCalmon, Thai Le, Sarra Alqahtani, and Dongwon Lee. Caps: Comprehensible abstract policy summaries for explaining reinforcement learning agents. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022.

International Conference on Brain Informatics

• Juhyung Ha, Nian Wang, Surendra Maharjan, and Xuhong Zhang. Super-Resolution MRH Reconstruction for Mouse Models. In International Conference on Brain Informatics, 215–226, 2023.

International Conference on Case-based Reasoning (ICCBR)

• Zachary Wilkerson, David Leake, David J. Crandall, and Benjamin Wilkerson. Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification. In International Conference on Case-based Reasoning (ICCBR), 2025.
• Vibhas Vats, Zachary Wilkerson, Hiroki Sato, David Leake, and David J. Crandall. Learning Case Features with Proxy-Guided Deep Neural Networks. In International Conference on Case-based Reasoning (ICCBR), 2025.

• Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, and David J. Crandall. Network Implementation of CBR: Case Study of a Neural Network K-NN. In International Conference on Case-based Reasoning (ICCBR), 2024.
• Zachary Wilkerson, David Leake, Vibhas Vats, and David J. Crandall. Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach. In International Conference on Case-based Reasoning (ICCBR), 2024.
• Kaitlynne Wilkerson and David Leake. On implementing case-based reasoning with large language models. In International Conference on Case-based Reasoning (ICCBR), 404–417, 2024.

• Zachary Wilkerson, Vibhas Vats, Karan Acharya, David Leake, and David J. Crandall. Examining the Impact of Network Architecture on Extracted Feature Quality for CBR. In International Conference on Case-based Reasoning (ICCBR), 2023.
• David Leake and Brian Schack. Towards addressing problem-distribution drift with case discovery. In International Conference on Case-based Reasoning (ICCBR), 244–259, 2023.

• David Leake, Zachary Wilkerson, and David J. Crandall. Extracting Case Indices from Convolutional Neural Networks: A Comparative Study. In International Conference on Case-based Reasoning (ICCBR), 2022.

• Xiaomeng Ye, David Leake, Vahid Jalali, and David J. Crandall. Learning Adaptations for Case-Based Classification: A Neural Network Approach. In International Conference on Case-based Reasoning (ICCBR), 2021.
• Zachary Wilkerson, David Leake, and David J. Crandall. On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval. In International Conference on Case-based Reasoning (ICCBR), 2021.
• David Leake and Xiaomeng Ye. Harmonizing Case Retrieval and Adaptation with Alternating Optimization. In International Conference on Case-based Reasoning (ICCBR), 125–139, 2021.

• Xiaomeng Ye, David Leake, William Huibregtse, and Mehmet Dalkilic. Applying Class-to-Class Siamese Networks to Explain Classifications with Supportive and Contrastive Cases. In International Conference on Case-based Reasoning (ICCBR), 2020.

International Conference on Cloud Computing (CLOUD)

• Prateek Sharma and Vikram Jadhao. Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms. In International Conference on Cloud Computing (CLOUD), 751–753, 2021.

International Conference on Computational Creativity (ICCC)

• Xiaomeng Ye, Ziwei Zhao, David Leake, and David J. Crandall. Generation and Evaluation of Creative Images from Limited Data: A Class-to-Class VAE Approach. In International Conference on Computational Creativity (ICCC), 2022.

International Conference on Computational Linguistics

• Ludovic Mompelat, Daniel Dakota, and Sandra Kübler. How to Parse a Creole: When Martinican Creole meets French. In International Conference on Computational Linguistics, 4397–4406, 2022.

International Conference on Computational Science

• Bruno Magalhães, Michael Hines, Thomas Sterling, and Felix Schürmann. Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks. In International Conference on Computational Science, 94–108, 2020.

International Conference on Computer-Supported Collaborative Learning (CSCL)

• Megan Humburg, Daeun Hong, Mengxi Zhou, Chen Feng, Joshua Danish, Cindy E Hmelo-Silver, Tianshu Wang, Krista Glazewski, Yeo Jin Kim, Vikram Kumaran, and others. Using AI-driven Conversational Agents to Support Student-Led Collaborative Scientific Inquiry. In International Conference on Computer-Supported Collaborative Learning (CSCL), 2025.

• Megan Humburg, Dalila Dragnić-Cindrić, Cindy E Hmelo-Silver, Krista Glazewski, and James Lester. Youth Perspectives on the Roles and Risks of AI in Their Classrooms. In International Conference on Computer-Supported Collaborative Learning (CSCL), 2024.

International Conference on Database Theory

• Qin Zhang and Mohsen Heidari. Quantum Data Sketches. In International Conference on Database Theory, 1, 2025.

International Conference on Field-Programmable Logic and Applications (FPL)

• Chengming Zhang, Tong Geng, Anqi Guo, Jiannan Tian, Martin Herbordt, Ang Li, and Dingwen Tao. H-GCN: A graph convolutional network accelerator on versal acap architecture. In International Conference on Field-Programmable Logic and Applications (FPL), 2022.

International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS)

• Ahmad Faiz, Lei Jiang, and Fan Chen. Special session: End-to-end carbon footprint assessment and modeling of deep learning. In International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS), 7–10, 2024.

International Conference on Human-Agent Interaction

• Zaiqiao Ye, Wei-Chu Chen, and Selma Sabanovic. Dressing up AIBO: An Exploration of User-generated Content of Robot Clothing on Twitter. In International Conference on Human-Agent Interaction, 81–89, 2023.

International Conference on Human-Machine Systems (ICHMS)

• Timothy Sellers, Tingjun Lei, Chaomin Luo, Lantao Liu, and Daniel W Carruth. Enhancing human-robot cohesion through hat methods: A crowd-avoidance model for safety aware navigation. In International Conference on Human-Machine Systems (ICHMS), 1–6, 2024.

International Conference on Intelligent Data and Security (IDS)

• Zhixin Li, Rui Zhu, Zihao Wang, Jiale Li, Kaiyuan Liu, Yue Qin, Yongming Fan, Mingyu Gu, Zhihui Lu, Jie Wu, and others. FairFix: Enhancing Fairness of Pre-Trained Deep Neural Networks with Scarce Data Resources. In International Conference on Intelligent Data and Security (IDS), 14–20, 2024.

International Conference on Language Resources and Evaluation (LREC)

• Zuoyu Tian and Sandra Kübler. Offensive Language Detection Using Brown Clustering. In International Conference on Language Resources and Evaluation (LREC), 2020.

International Conference on Learning Representations (ICLR)

• Tianyuan Jin, Qin Zhang, and Dongruo Zhou. Breaking the $$\backslash $log (1/\Delta_2) $ Barrier: Better Batched Best Arm Identification with Adaptive Grids. In International Conference on Learning Representations (ICLR), 2025.
• Zhiyong Wang, Dongruo Zhou, John CS Lui, and Wen Sun. Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds. In International Conference on Learning Representations (ICLR), 2025.

• Manju Garimella, Denizhan Pak, Justin N Wood, and Samantha Wood. A newborn embodied Turing test for comparing object segmentation across animals and machines. In International Conference on Learning Representations (ICLR), 2024.
• Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, and Quanquan Gu. Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. In International Conference on Learning Representations (ICLR), 2024.

• Yijie Wang, Y. Zhou, and Jianzhu Ma. Learning Sparse Group Models Through Boolean Relaxation. In International Conference on Learning Representations (ICLR), 2023.

• Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, and Hongning Wang. Learning Neural Contextual Bandits through Perturbed Rewards. In International Conference on Learning Representations (ICLR), 2022.

• Qian Lou, Yilin Shen, Hongxia Jin, and Lei Jiang. SAFENet: A secure, accurate and fast neural network inference. In International Conference on Learning Representations (ICLR), 2020.

International Conference on Machine Learning (ICML)

• Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C.S. Lui, and Dongruo Zhou. Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. In International Conference on Machine Learning (ICML), 2025.
• Zhiyong Wang, Chen Yang, John C.S. Lui, and Dongruo Zhou. Provable Zero-Shot Generalization in Offline Reinforcement Learning. In International Conference on Machine Learning (ICML), 2025.

• Junkai Zhang, Weitong Zhang, Dongruo Zhou, and Quanquan Gu. Uncertainty-Aware Reward-Free Exploration with General Function Approximation. In International Conference on Machine Learning (ICML), 60414–60445, 2024.

• Qiwei Di, Jiafan He, Dongruo Zhou, and Quanquan Gu. Nearly minimax optimal regret for learning linear mixture stochastic shortest path. In International Conference on Machine Learning (ICML), 7837–7864, 2023.
• Heyang Zhao, Dongruo Zhou, Jiafan He, and Quanquan Gu. Optimal online generalized linear regression with stochastic noise and its application to heteroscedastic bandits. In International Conference on Machine Learning (ICML), 42259–42279, 2023.
• Jiafan He, Heyang Zhao, Dongruo Zhou, and Quanquan Gu. Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. In International Conference on Machine Learning (ICML), 2023.

• Brandon G. Jacques, Zoran Tiganj, Aakash Sarkar, Marc W. Howard, and Per B. Sederberg. A deep convolutional neural network that is invariant to time rescaling. In International Conference on Machine Learning (ICML), volume 162, 9729–9738, 2022.
• Dongruo Zhou and Quanquan Gu. Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. In International Conference on Machine Learning (ICML), 27143–27158, 2022.

• Qian Lou and Lei Jiang. Hemet: A homomorphic-encryption-friendly privacy-preserving mobile neural network architecture. In International Conference on Machine Learning (ICML), 2021.
• Mohsen Heidari, Jithin Sreedharan, Gil I Shamir, and Wojciech Szpankowski. Finding relevant information via a discrete fourier expansion. In International Conference on Machine Learning (ICML), 4181–4191, 2021.

• Kefan Dong, Yingkai Li, Qin Zhang, and Yuan Zhou. Multinomial logit bandit with low switching cost. In International Conference on Machine Learning (ICML), 2607–2615, 2020.

• Chao Tao, Saúl Blanco, and Yuan Zhou. Best arm identification in linear bandits with linear dimension dependency. In International Conference on Machine Learning (ICML), 2018.

International Conference on Parallel Processing and Applied Mathematics

• Bibrak Qamar Chandio, Maciej Brodowicz, and Thomas Sterling. Exploring the Design Space for Message-Driven Systems for Dynamic Graph Processing using CCA. In International Conference on Parallel Processing and Applied Mathematics, 85–98, 2024.

International Conference on Parsing Technologies

• Daniel Dakota, Zeeshan Ali Sayyed, and Sandra Kübler. Bidirectional Domain Adaptation Using Weighted Multi-Task Learning. In International Conference on Parsing Technologies, 2021.

International Conference on Social Media and Society (SMSociety)

• Shujon Naha, Seung Woo Chae, Noriko Hara, and David J. Crandall. Exploring Medical YouTubers' Parasocial Visual Cues in Their COVID-related Videos. In International Conference on Social Media and Society (SMSociety), 2024.

International Conference on Social Robotics

• Randy Gomez, Eleanor Sandry, Selma Šabanović, Deborah Szapiro, Vicky Charisi, Daniel Serrano, Thomas H Weisswange, Matthew P Aylett, Guangliang Li, Pourang Irani, and others. Three Principles for Social Robots as Embodied Mediators. In International Conference on Social Robotics, 78–90, 2024.

• Eric Nichols, Sarah Rose Siskind, Waki Kamino, Selma Šabanović, and Randy Gomez. Iterative Design of an Emotive Voice for the Tabletop Robot Haru. In International Conference on Social Robotics, 362–374, 2021.

• Randy Gomez, Deborah Szapiro, Luis Merino, Heike Brock, Keisuke Nakamura, and Selma Sabanovic. Emoji to robomoji: exploring affective telepresence through haru. In International Conference on Social Robotics, 652–663, 2020.
• Swapna Joshi, Sawyer Collins, Waki Kamino, Randy Gomez, and Selma Šabanović. Social robots for socio-physical distancing. In International Conference on Social Robotics, 440–452, 2020.

International Conference on Tourism Research

• Youngjun Park, Jisun An, and Dongman Lee. A Model for Monthly, Local-Level Airbnb Changes Using Public Dataset. In International Conference on Tourism Research, 2025.

International FLAIRS Conference

• Caleb Kisby, Saúl Blanco, and Lawrence Moss. The Logic of Hebbian Learning. In International FLAIRS Conference, 2022.

International Joint Conference on Artificial Intelligence (IJCAI)

• Xiaomeng Ye, David Leake, Yu Wang, and David J. Crandall. Run Like a Neural Network, Explain Like k-Nearest Neighbor. In International Joint Conference on Artificial Intelligence (IJCAI), 2025.

International Joint Conference on Neural Networks (IJCNN)

• Boli Fang, Zhenghao Peng, Hao Sun, and Qin Zhang. Meta Proximal Policy Optimization for Cooperative Multi-Agent Continuous Control. In International Joint Conference on Neural Networks (IJCNN), 2022.

International Symposium of Robotics Research (ISRR)

• Md Al-Masrur Khan, Zheng Chen, and Lantao Liu. C2DA: Contrastive and Context-aware Domain Adaptive Semantic Segmentation. In International Symposium of Robotics Research (ISRR), 2024.

International Symposium on Artificial Intelligence and Mathematics (ISAIM)

• Matthias Scheutz, Kamal Premaratne, Lawrence S. Moss, and Avery Caulfield. Probabilistic `If-Then' Rules: On Bayesian Conditionals and Probabilistic Implications. In International Symposium on Artificial Intelligence and Mathematics (ISAIM), 2022.

International Symposium on Bioinformatics Research and Applications

• Yuan Xie, Yulong Pei, Yun Lu, Haixu Tang, and Yuan Zhou. Learning Structural Genetic Information via Graph Neural Embedding. In International Symposium on Bioinformatics Research and Applications, 250–261, 2020.

International Symposium on Distributed Autonomous Robotic Systems

• Junhong Xu, Durgakant Pushp, Kai Yin, and Lantao Liu. Decision-making among bounded rational agents. In International Symposium on Distributed Autonomous Robotic Systems, 273–285, 2022.

International Symposium on Experimental Robotics

• Lantao Liu. POVNav: A Pareto-Optimal Mapless Visual Navigator. In International Symposium on Experimental Robotics, volume 30, 250, 2024.

• Durgakant Pushp, Zheng Chen, Chaomin Luo, Jason M Gregory, and Lantao Liu. Povnav: A pareto-optimal mapless visual navigator. In International Symposium on Experimental Robotics, 250–263, 2023.

International Symposium on Quality Electronic Design (ISQED)

• Mengxin Zheng, Fan Chen, Lei Jiang, and Qian Lou. Priml: An electro-optical accelerator for private machine learning on encrypted data. In International Symposium on Quality Electronic Design (ISQED), 1–7, 2023.

Interspeech

• Binh Nguyen and Thai Le. Turing's Echo: Investigating Linguistic Sensitivity of Deepfake Voice Detection via Gamification. In Interspeech, 2145–2146, 2025.

• Aswin Sivaraman, Sunwoo Kim, and Minje Kim. Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification. In Interspeech, 2021.

• Aswin Sivaraman and Minje Kim. Sparse Mixture of Local Experts for Efficient Speech Enhancement. In Interspeech, 2020.

Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

• Thai Le, Noseong Park, and Dongwon Lee. A sweet rabbit hole by DARCY: Using honeypots to detect universal trigger's adversarial attacks. In Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021, 3831–3844, 2021.

Joint Conference on Lexical and Computational Semantics (SEM)

• Zeming Chen, Qiyue Gao, and Lawrence S. Moss. NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning. In Joint Conference on Lexical and Computational Semantics (SEM), 78–88, 2021.

Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

• Daniel Dakota and Sandra Kübler. Bits and Pieces: Investigating the Effects of Subwords in Multi-task Parsing Across Languages and Domains. In Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2397–2409, 2024.

Language Resources and Evaluation Conference

• Haining Wang and Allen Riddell. CCTAA: A Reproducible Corpus for Chinese Authorship Attribution Research. In Language Resources and Evaluation Conference, 5889–5893, 2022.

Meeting of the Society for Computation in Linguistics (SCIL)

• Daniel Dakota and Sandra Kübler. What's in a Span? Evaluating the Creativity of a Span-Based Neural Constituency Parser. In Meeting of the Society for Computation in Linguistics (SCIL), 2021.

• Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence Moss, and Sandra Kübler. MonaLog: A Lightweight System for Natural Language Inference Based on Monotonicity. In Meeting of the Society for Computation in Linguistics (SCIL), 2020.

Object-Oriented Programming, Systems, Languages and Applications (OOPSLA)

• David Chiang, Colin McDonald, and Chung-chieh Shan. Exact Recursive Probabilistic Programming. In Object-Oriented Programming, Systems, Languages and Applications (OOPSLA), 2023.

Pacific-Asia Conference on Knowledge Discovery and Data Mining

• Nicholas Majeske and Ariful Azad. Multi-modal recurrent graph neural networks for spatiotemporal forecasting. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 144–157, 2024.

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)

• Adaku Uchendu, Saranya Venkatraman, Thai Le, and Dongwon Lee. Catch me if you gpt: Tutorial on deepfake texts. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts), 1–7, 2024.

Proceedings of the AAAI Symposium Series

• Long-Jing Hsu and Selma Šabanović. Designing AI for Partnership in Care. In Proceedings of the AAAI Symposium Series, volume 4, 158–162, 2024.

Reinforcement Learning Conference

• Sahaj Singh Maini and Zoran Tiganj. Reinforcement Learning with Adaptive Temporal Discounting. In Reinforcement Learning Conference, 2025.

Robotics: Science and Systems (RSS)

• Weizhe Chen, Lantao Liu, and Roni Khardon. POAM: Probabilistic Online Attentive Mapping for Efficient Robotic Information Gathering. In Robotics: Science and Systems (RSS), 2024.

• Mahmoud Ali, Hassan Jardali, Nicholas Roy, and Lantao Liu. Autonomous navigation, mapping and exploration with gaussian processes. In Robotics: Science and Systems (RSS), 2023.

• Weizhe Chen, Roni Khardon, and Lantao Liu. AK: Attentive Kernel for Information Gathering. In Robotics: Science and Systems (RSS), 2022.
• Zheng Chen, Durgakant Pushp, and Lantao Liu. CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation. In Robotics: Science and Systems (RSS), 2022.

Swedish Language Technology Conference and NLP4CALL

• Nils Hjortn\aes , Daniel Dakota, Sandra Kübler, and Francis Tyers. Evaluating Automatic Pronunciation Scoring with Crowd-sourced Speech Corpus Annotations. In Swedish Language Technology Conference and NLP4CALL, 67–77, 2024.

The Web Conference

• Seongwoon Kim, Yong-Yeol Ahn, and Jaehyuk Park. Labor space: A unifying representation of the labor market via large language models. In The Web Conference, 2441–2451, 2024.
• Md Saidul Hoque Anik, Pranav Badhe, Rohit Gampa, and Ariful Azad. isplib: A library for accelerating graph neural networks using auto-tuned sparse operations. In The Web Conference, 778–781, 2024.
• Selahattin Akkas and Ariful Azad. Gnnshap: Scalable and accurate gnn explanation using shapley values. In The Web Conference, 827–838, 2024.

• Jooyoung Lee, Thai Le, Jinghui Chen, and Dongwon Lee. Do language models plagiarize? In The Web Conference, 3637–3647, 2023.
• Fan Huang, Haewoon Kwak, and Jisun An. Chain of explanation: New prompting method to generate quality natural language explanation for implicit hate speech. In The Web Conference, 90–93, 2023.

• Thai Le, Long Tran-Thanh, and Dongwon Lee. Socialbots on fire: Modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning. In The Web Conference, 545–554, 2022.

USENIX Security Symposium (USENIX Security)

• Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, XiaoFeng Wang, and Haixu Tang. $\$Sharpness-Aware$\$ Initialization: Improving Differentially Private Machine Learning from First Principles. In USENIX Security Symposium (USENIX Security), 3103–3122, 2025.

• Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, and XiaoFeng Wang. $\$DPAdapter$\$: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. In USENIX Security Symposium (USENIX Security), 991–1008, 2024.
• Dandan Xu, Di Tang, Yi Chen, XiaoFeng Wang, Kai Chen, Haixu Tang, and Longxing Li. Racing on the Negative Force: Efficient Vulnerability $\$Root-Cause$\$ Analysis through Reinforcement Learning on Counterexamples. In USENIX Security Symposium (USENIX Security), 4229–4246, 2024.
• Zilong Lin, Jian Cui, Xiaojing Liao, and XiaoFeng Wang. Malla: Demystifying real-world large language model integrated malicious services. In USENIX Security Symposium (USENIX Security), 4693–4710, 2024.

• Yi Chen, Di Tang, Yepeng Yao, Mingming Zha, XiaoFeng Wang, Xiaozhong Liu, Haixu Tang, and Baoxu Liu. Sherlock on specs: Building $\$LTE$\$ conformance tests through automated reasoning. In USENIX Security Symposium (USENIX Security), 3529–3545, 2023.
• Yuhong Nan, Xueqiang Wang, Luyi Xing, Xiaojing Liao, Ruoyu Wu, Jianliang Wu, Yifan Zhang, and XiaoFeng Wang. Are you spying on me?$\$Large-Scale$\$ analysis on $\$IoT$\$ data exposure through companion apps. In USENIX Security Symposium (USENIX Security), 6665–6682, 2023.

• Yi Chen, Di Tang, Yepeng Yao, Mingming Zha, XiaoFeng Wang, Xiaozhong Liu, Haixu Tang, and Dongfang Zhao. Seeing the forest for the trees: Understanding security hazards in the $\$3GPP$\$ ecosystem through intelligent analysis on change requests. In USENIX Security Symposium (USENIX Security), 17–34, 2022.

• Yuxuan Chen, Xuejing Yuan, Jiangshan Zhang, Yue Zhao, Shengzhi Zhang, Kai Chen, and XiaoFeng Wang. Devil's Whisper: A General Approach for Physical Adversarial Attacks against Commercial Black-Box Speech Recognition Devices. In USENIX Security Symposium (USENIX Security), 2020.

iConference

• Kahyun Choi. Bimodal Music Subject Classification via Context-Dependent Language Models. In iConference, 68–77, 2021.

Book chapters

• Gonzalo A García, Leigh M Levinson, Guillermo Pérez, Manuel Castro, José Gabriel Amores, Gloria Álvarez, Randy Gomez, and Selma Šabanović. Exploring Key Challenges in Child-Robot Interaction Using Haru4Kids: Engagement, Language Understanding, and Privacy. In Current State and Future Perspective in Human-Robot Interaction. IntechOpen, 2025.
• Leigh Levinson, Eli McGraw, Randy Gomez, and Selma Šabanović. A Spectrum of Perceived Sociality: A Dynamical and Enactive Centering of Interactions Between Children and Robots. In Social Robots with AI: Prospects, Risks, and Responsible Methods, pages 110–121. IOS Press, 2025.
• Pawan Kumar Goel and Satya Prakash Yadav. Bridging Minds and Machines: An Introduction to NLP in Mental Health. In Demystifying the Role of Natural Language Processing (NLP) in Mental Health, pages 1–22. IGI Global Scientific Publishing, 2025.

• Katherine M. Tsui, Sarah Cohen, Selma Sabanovic, Alex Alspach, Rune Baggett, David J. Crandall, and Steffi Paepcke. Uncovering Older Adult Needs: Applying User-Centered Research Methodologies to Inform Robotics Development and a Call to Action. In Human-Robot Interaction – A Multidisciplinary Overview. IntechOpen, 2024.
• Selma Šabanović. Designing for Social Embeddedness: Mutually Shaping Robots and Society. In Designing Interactions with Robots, pages 38–69. Chapman and Hall/CRC, 2024.
• Linda B Smith and Larissa k Samuelson. Perceiving and Remembering: Category Stability, Variability and Development. In Knowledge, concepts and categories, pages 161–196. Psychology Press, 2024.

• David Leake, Zachary Wilkerson, Xiaomeng Ye, and David J. Crandall. Enhancing Case-Based Reasoning with Neural Networks. In Compendium of Neurosymbolic Artificial Intelligence, pages 387–409. IOS Press, 2023.
• Fan Chen. On-Chip DNN Training for Direct Feedback Alignment in FeFET. In Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Hardware Architectures, pages 317–335. Springer, 2023.

• Lawrence S. Moss. Algebra and Language: Reasons for (Dis)content. In Algebraic Structures and Natural Language, pages 227–250. Taylor and Francis, 2022.
• Johan Bollen, Marijn ten Thij, Lorenzo Lorenzo-Luaces, and Lauren A Rutter. Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data. In Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project, pages 265–287. Springer, 2022.

Books

• Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Šabanović. Human-robot interaction: An introduction. Cambridge University Press, 2024.

Journal articles

ACM Computing Surveys

• Vibhas Vats and David J. Crandall. Geometric Constraints in Deep Learning Frameworks: A Survey. ACM Computing Surveys, May 2025.

ACM SIGKDD Explorations Newsletter

• Adaku Uchendu, Thai Le, and Dongwon Lee. Attribution and obfuscation of neural text authorship: A data mining perspective. ACM SIGKDD Explorations Newsletter, 25(1):1–18, 2023.

ACM Transactions on Accessible Computing

• Houda Elmimouni, Selma Šabanović, and Jennifer A Rode. Navigating the cyborg classroom: telepresence robots, accessibility challenges, and inclusivity in the classroom. ACM Transactions on Accessible Computing, 17(2):1–21, 2024.

ACM Transactions on Computing for Healthcare

• Cristina Bosco, Fereshtehossadat Shojaei, Alec A Theisz, Vivian Nguyen, Haoru Song, Ruixiang Han, John A Osorio Torres, Darshil Chheda, Jenny Lin, Xinran Peng, and others. “I don’t see anything specifically about Black/African Americans.” Testing an Alzheimer-specific generative AI tool tailored for African American/Black communities. ACM Transactions on Computing for Healthcare, 2025.

ACM Transactions on Human-Robot Interaction

• Natasha Randall and Selma Šabanović. Do You Want Me?: Exploring Differences in Consumer Home Robot Preferences, Perceptions, and Purchase Intent. ACM Transactions on Human-Robot Interaction, 14(3):1–39, 2025.

ACM Transactions on Knowledge Discovery from Data

• Yunji Liang, Lei Liu, Luwen Huangfu, Sagar Samtani, Zhiwen Yu, and Daniel D Zeng. Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive Learning. ACM Transactions on Knowledge Discovery from Data, 18(3):1–24, 2023.

ACM Transactions on Management Information Systems (TMIS)

• Yidong Chai, Hongyan Liu, Jie Xu, Sagar Samtani, Yuanchun Jiang, and Haoxin Liu. A Multi-Label Classification with An Adversarial-Based Denoising Autoencoder for Medical Image Annotation. ACM Transactions on Management Information Systems (TMIS), 14(2):1–21, 2023.

• Saurav Chakraborty, Agnieszka Onuchowska, Sagar Samtani, Wolfgang Jank, and Brandon Wolfram. Machine learning for automated industrial IoT attack detection: An efficiency-complexity trade-off. ACM Transactions on Management Information Systems (TMIS), 12(4):1–28, 2021.
• Sagar Samtani, Murat Kantarcioglu, and Hsinchun Chen. A multi-disciplinary perspective for conducting artificial intelligence-enabled privacy analytics: Connecting data, algorithms, and systems. ACM Transactions on Management Information Systems (TMIS), 12(1):1–18, 2021.

• Sagar Samtani, Murat Kantarcioglu, and Hsinchun Chen. Trailblazing the artificial intelligence for cybersecurity discipline: a multi-disciplinary research roadmap. ACM Transactions on Management Information Systems (TMIS), 11(4):1–19, 2020.

ACM Transactions on Privacy and Security

• Yuxuan Chen, Jiangshan Zhang, Xuejing Yuan, Shengzhi Zhang, Kai Chen, Xiaofeng Wang, and Shanqing Guo. Sok: A modularized approach to study the security of automatic speech recognition systems. ACM Transactions on Privacy and Security, 25(3):1–31, 2022.

ACM on Measurement and Analysis of Computing Systems

• Haoran Lu, Qingchuan Zhao, Yongliang Chen, Xiaojing Liao, and Zhiqiang Lin. Detecting and measuring aggressive location harvesting in mobile apps via data-flow path embedding. ACM on Measurement and Analysis of Computing Systems, 7(1):1–27, 2023.

ACM/JMS Journal of Data Science

• Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, and Dongruo Zhou. Batched neural bandits. ACM/JMS Journal of Data Science, 1(1):1–18, 2024.

ACS Nano

• Joshua Smith, Md Alimoor Reza, Nathanael Smith, Jianxin Gu, Maha Ibrar, David Crandall, and Sara Skrabalak. Plasmonic Anti-counterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning. ACS Nano, 2021.

AI Magazine

• Dhabaleswar K Panda, Vipin Chaudhary, Eric Fosler-Lussier, Raghu Machiraju, Amit Majumdar, Beth Plale, Rajiv Ramnath, Ponnuswamy Sadayappan, Neelima Savardekar, and Karen Tomko. Creating intelligent cyberinfrastructure for democratizing AI. AI Magazine, 45(1):22–28, 2024.

AIDS and Behavior

• Andy Edinger, Danny Valdez, Eric Walsh-Buhi, and Johan Bollen. Deep learning for topical trend discovery in online discourse about Pre-Exposure Prophylaxis (PrEP). AIDS and Behavior, pages 1–11, 2022.

Advanced Functional Materials

• Maha Ibrar, Sheng-Yuan Huang, Zachery McCurtain, Shujon Naha, David J. Crandall, Stephen C. Jacobson, and Sara E. Skrabalak. Modular Anti-counterfeit Tags Formed by Template-Assisted Self-Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning. Advanced Functional Materials, 2024.

Advanced Robotics

• Gonzalo A García, Guillermo Pérez, Rohan K Laycock-Narayan, Leigh Levinson, J Gabriel Amores, Gloria Alvarez-Benito, Manuel Castro-Malet, Mario Castaño-Ocaña, Marta J Lopez-Gonzalez de Quevedo, Ricardo Durán-Viñuelas, and others. Preliminary study on the feasibility of approximating children's engagement level from their emotions estimation by a picture-based, three-model AI in a family-robot cohabitation scenario. Advanced Robotics, 38(23):1710–1728, 2024.
• Guillermo Pérez García, Gonzalo A García, Manuel Castro, Mario Castaño, Marta J López-González de Quevedo, Ricardo Durán, Luis Pérez, J Gabriel Amores, Gloria Álvarez, Leigh Michelle Levinson, and others. A multi-site language study on child-robot dialogues. Advanced Robotics, 38(19-20):1486–1500, 2024.

• Jinjae Lee, Casey C Bennett, Cedomir Stanojevic, Seongcheol Kim, Zachary Henkel, Kenna Baugus, Jennifer A Piatt, Cindy Bethel, and Selma Sabanovic. Detecting cultural identity via robotic sensor data to understand differences during human-robot interaction. Advanced Robotics, 37(22):1446–1459, 2023.

Advances in Methods and Practices in Psychological Science

• Russell J Boag, Reilly J Innes, Niek Stevenson, Giwon Bahg, Jerome R Busemeyer, Gregory E Cox, Chris Donkin, Michael J Frank, Guy E Hawkins, Andrew Heathcote, and others. An expert guide to planning experimental tasks for evidence-accumulation modeling. Advances in Methods and Practices in Psychological Science, 2025.

Advances in Neural Information Processing Systems (NeurIPS)

• Shreyas Fadnavis, Joshua Batson, and Eleftherios Garyfallidis. Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​. Advances in Neural Information Processing Systems (NeurIPS), 33:16293–16303, 2020.

Alcohol

• Molly Rosenberg, Sina Kianersi, Maya Luetke, Kristen Jozkowski, Lucia Guerra-Reyes, Patrick C Shih, Peter Finn, and Christina Ludema. Wearable alcohol monitors for alcohol use data collection among college students: feasibility and acceptability. Alcohol, 111:75–83, 2023.

American Journal of Recreation Therapy

• Lori Eldridge, Shinichi Nagata, Jennifer Piatt, Cedomir Stanojevic, Selma Šabanović, Casey Bennett, Natasha Randall, and others. Utilization of socially assistive robots in recreational therapy. American Journal of Recreation Therapy, 19(2):35–45, 2020.

Analytical chemistry

• Kaiyuan Liu, Sujun Li, Lei Wang, Yuzhen Ye, and Haixu Tang. Full-spectrum prediction of peptides tandem mass spectra using deep neural network. Analytical chemistry, 92(6):4275–4283, 2020.

Annual Review of Analytical Chemistry

• Yuhui Hong, Yuzhen Ye, and Haixu Tang. Machine Learning in Small-Molecule Mass Spectrometry. Annual Review of Analytical Chemistry, 2025.

Annual Review of Vision Science

• Justin N Wood, Lalit Pandey, and Samantha MW Wood. Digital twin studies for reverse engineering the origins of visual intelligence. Annual Review of Vision Science, 10(1):145–170, 2024.

Annual review of biomedical data science

• Rion Brattig Correia, Ian B Wood, Johan Bollen, and Luis M Rocha. Mining social media data for biomedical signals and health-related behavior. Annual review of biomedical data science, 3:433–458, 2020.

Applied Network Science

• John Bollenbacher, Diogo Pacheco, Pik-Mai Hui, Yong-Yeol Ahn, Alessandro Flammini, and Filippo Menczer. On the challenges of predicting microscopic dynamics of online conversations. Applied Network Science, 6:1–21, 2021.

Art Therapy

• Fereshtehossadat Shojaei, John Osorio Torres, and Patrick C Shih. Exploring the integration of technology in art therapy: insights from interviews with art therapists. Art Therapy, 42(2):112–118, 2025.

Artificial Life

• Randall D Beer. (A) Life as It Could Be. Artificial Life, 30(4):539–545, 2024.

Autonomous Robots

• Zheng Chen, Durgakant Pushp, Jason M Gregory, and Lantao Liu. Pseudo-trilateral adversarial training for domain adaptive traversability prediction. Autonomous Robots, 47(8):1155–1174, 2023.

BMC bioinformatics

• Marcin Malec, Hasan Kurban, and Mehmet Dalkilic. CcImpute: An accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data. BMC bioinformatics, 23(1):291, 2022.
• Jeremy J Yang, Christopher R Gessner, Joel L Duerksen, Daniel Biber, Jessica L Binder, Murat Ozturk, Brian Foote, Robin McEntire, Kyle Stirling, Ying Ding, and others. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC bioinformatics, 23(1):37, 2022.

Behavior Research Methods

• Billy Dickson, Sahaj Singh Maini, Craig Sanders, Robert Nosofsky, and Zoran Tiganj. Comparing perceptual judgments in large multimodal models and humans. Behavior Research Methods, 57(7):1–13, 2025.

• Calvin Isch, Marijn Ten Thij, Peter M Todd, and Johan Bollen. Quantifying changes in societal optimism from online sentiment. Behavior Research Methods, 55(1):176–184, 2023.

• Charles Maitha, Jesse C Goode, Danielle P Maulucci, Suha MS Lasassmeh, Chen Yu, Linda B Smith, and Jeremy I Borjon. An open-source, wireless vest for measuring autonomic function in infants. Behavior Research Methods, 52:2324–2337, 2020.

Bioinformatics

• Kaiyuan Liu, Chenghua Tao, Yuzhen Ye, and Haixu Tang. SpecEncoder: deep metric learning for accurate peptide identification in proteomics. Bioinformatics, 40(Supplement_1):i257–i265, 2024.

• Yuhui Hong, Sujun Li, Christopher J Welch, Shane Tichy, Yuzhen Ye, and Haixu Tang. 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations. Bioinformatics, 2023.

Bioinformatics Advances

• Mahsa Monshizadeh, Yuhui Hong, and Yuzhen Ye. Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human microbiome. Bioinformatics Advances, 5(1):vbae203, 2025.

Biological Cybernetics

• Randall D Beer. Codimension-2 parameter space structure of continuous-time recurrent neural networks. Biological Cybernetics, 116(4):501–515, 2022.

Biosystems

• Randall D Beer and Ezequiel A Di Paolo. The theoretical foundations of enaction: Precariousness. Biosystems, 223:104823, 2023.

Briefings in Bioinformatics

• Marco Ruscone, Andrea Checcoli, Randy Heiland, Emmanuel Barillot, Paul Macklin, Laurence Calzone, and Vincent Noël. Building multiscale models with PhysiBoSS, an agent-based modeling tool. Briefings in Bioinformatics, 25(6):bbae509, 2024.

Cell Reports

• Youngheun Jo, Farnaz Zamani Esfahlani, Joshua Faskowitz, Evgeny J Chumin, Olaf Sporns, and Richard F Betzel. The diversity and multiplexity of edge communities within and between brain systems. Cell Reports, 37(7):110032, 2021.

Cognition

• Erin M Anderson, Eric S Seemiller, and Linda B Smith. Scene saliencies in egocentric vision and their creation by parents and infants. Cognition, 229:105256, 2022.

• Jennifer S Trueblood, Quentin Eichbaum, Adam C Seegmiller, Charles Stratton, Payton O'Daniels, and William R Holmes. Disentangling prevalence induced biases in medical image decision-making. Cognition, 212:104713, 2021.

• Justin N Wood and Samantha MW Wood. One-shot learning of view-invariant object representations in newborn chicks. Cognition, 2020.

Cognition and Instruction

• Andrea Gomoll, Cindy E Hmelo-Silver, and Selma Šabanović. Co-constructing professional vision: Teacher and researcher learning in co-design. Cognition and Instruction, 40(1):7–26, 2022.

Cognitive Research: Principles and Implications

• Eeshan Hasan, Erik Duhaime, and Jennifer S Trueblood. Boosting wisdom of the crowd for medical image annotation using training performance and task features. Cognitive Research: Principles and Implications, 9(1):31, 2024.

Communication Research

• Maria D Molina, Jinping Wang, S Shyam Sundar, Thai Le, and Carlina DiRusso. Reading, commenting and sharing of fake news: How online bandwagons and bots dictate user engagement. Communication Research, 50(6):667–694, 2023.

Communications of the ACM (CACM)

• Taha Yasseri and Filippo Menczer. Can crowdsourcing rescue the social marketplace of ideas?Communications of the ACM (CACM), 66(9):42–45, 2023.

Computational Brain \& Behavior

• William R Holmes, Payton O’Daniels, and Jennifer S Trueblood. A joint deep neural network and evidence accumulation modeling approach to human decision-making with naturalistic images. Computational Brain & Behavior, 3:1–12, 2020.

Computational Linguistics

• Katerina Kalouli, Hai Hu, Alexander Frank Webb, Lawrence S Moss, and Valeria de Paiva. Curing the SICK and Other NLI Maladies. Computational Linguistics, 2023.

Computer-Aided Design and Applications

• Laura Loredana Micoli, Gabriele Guidi, and Giandomenico Caruso. Automatic 3D Modeling Process for Predefined Geometrical Categories Based on Convolutional Neural Network and Computer-Vision Analysis of Orthographic Images. Computer-Aided Design and Applications, 2024.

Computers in Human Behavior

• Marlena R Fraune, Benjamin C Oisted, Catherine E Sembrowski, Kathryn A Gates, Margaret M Krupp, and Selma Šabanović. Effects of robot-human versus robot-robot behavior and entitativity on anthropomorphism and willingness to interact. Computers in Human Behavior, 105:106220, 2020.

Concurrency and Computation: Practice and Experience

• Vibhatha Abeykoon, Geoffrey Fox, Minje Kim, Saliya Ekanayake, Supun Kamburugamuve, Kannan Govindarajan, Pulasthi Wickramasinghe, Niranda Perera, Chathura Widanage, Ahmet Uyar, and others. Stochastic gradient descent-based support vector machines training optimization on Big Data and HPC frameworks. Concurrency and Computation: Practice and Experience, 34(8):e6292, 2022.

Culturally Sustainable Social Robotics: Proceedings of Robophilosophy

• Selma Šabanović. Designing Companion Artifacts: The Relational Construction of Culture and Technology in Social Robotics. Culturally Sustainable Social Robotics: Proceedings of Robophilosophy, 2021.

Current Biology

• Kathryn Bonnen. Motion vision: Fish swimming to see. Current Biology, 33(1):R30–R32, 2023.

Data Intelligence

• Tingyi Wanyan, Hossein Honarvar, Ariful Azad, Ying Ding, and Benjamin S Glicksberg. Deep learning with heterogeneous graph embeddings for mortality prediction from electronic health records. Data Intelligence, 3(3):329–339, 2021.

Diachronica

• Patrícia Amaral, Hai Hu, and Sandra Kübler. Tracing semantic change with distributional methods: The contexts of \textit algo. Diachronica, 2022.

Digital Scholarship in the Humanities

• Hai Hu, Patrícia Amaral, and Sandra Kübler. Word Embeddings and Semantic Shifts in Historical Spanish: Methodological Considerations. Digital Scholarship in the Humanities, 2021.

Discourse, Context \& Media

• Holly Lopez and Sandra Kübler. Context in abusive language detection: On the interdependence of context and annotation of user comments. Discourse, Context & Media, 63:100848, 2025.

EPJ Data Science

• Alexander C Nwala, Alessandro Flammini, and Filippo Menczer. A language framework for modeling social media account behavior. EPJ Data Science, 12(1):33, 2023.

• Elise Jing and Yong-Yeol Ahn. Characterizing partisan political narrative frameworks about COVID-19 on Twitter. EPJ Data Science, 10(1):53, 2021.

Elife

• Karl S Muller, Kathryn Bonnen, Stephanie M Shields, Daniel P Panfili, Jonathan Matthis, and Mary M Hayhoe. Analysis of foothold selection during locomotion using terrain reconstruction. Elife, 12:RP91243, 2024.

Energy AI

• N Majeske, SS Vaidya, R Roy, A Rehman, H Sohrabpoor, T Miller, W Li, CR Fiddyment, A Gumennik, R Acharya, and others. Industrial energy forecasting using dynamic attention neural networks. Energy AI, 20:100504, 2025.

Engineering

• Yiran Chen, Yuan Xie, Linghao Song, Fan Chen, and Tianqi Tang. A survey of accelerator architectures for deep neural networks. Engineering, 6(3):264–274, 2020.

European Journal of Neuroscience

• Randall D Beer, Ann-Sophie Barwich, and Gabriel J Severino. Milking a spherical cow: Toy models in neuroscience. European Journal of Neuroscience, 60(10):6359–6374, 2024.

Exercise and Sport Sciences Reviews

• Daehyoung Lee, Georgia C Frey, and Patrick C Shih. Gamified Mobile Health Strategies for Promoting Physical Activity in Autistic Adults. Exercise and Sport Sciences Reviews, 53(2):68–76, 2025.

Experimental and Applied Acarology

• Oghenekaro Omodior, Mohammad R Saeedpour-Parizi, Md Khaledur Rahman, Ariful Azad, and Keith Clay. Using convolutional neural networks for tick image recognition–a preliminary exploration. Experimental and Applied Acarology, 84:607–622, 2021.

Frontiers in Computational Neuroscience

• Jason A Yoder, Cooper B Anderson, Cehong Wang, and Eduardo J Izquierdo. Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks. Frontiers in Computational Neuroscience, 2022.

• Erick Olivares, Eduardo J Izquierdo, and Randall D Beer. A neuromechanical model of multiple network rhythmic pattern generators for forward locomotion in C. elegans. Frontiers in Computational Neuroscience, 15:572339, 2021.

Frontiers in Digital Health

• Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L Fonseca, James Glazier, Tomas Helikar, Marti Jett-Tilton, Denise Kirschner, and others. Toward mechanistic medical digital twins: some use cases in immunology. Frontiers in Digital Health, 6:1349595, 2024.

• Cedomir Stanojevic, Casey C Bennett, Selma Sabanovic, Sawyer Collins, Kenna Baugus Henkel, Zachary Henkel, and Jennifer A Piatt. Conceptualizing socially-assistive robots as a digital therapeutic tool in healthcare. Frontiers in Digital Health, 5:1208350, 2023.

• Eric A Stahlberg, Mohamed Abdel-Rahman, Boris Aguilar, Alireza Asadpoure, Robert A Beckman, Lynn L Borkon, Jeffrey N Bryan, Colleen M Cebulla, Young Hwan Chang, Ansu Chatterjee, and others. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Frontiers in Digital Health, 4:1007784, 2022.

Frontiers in Immunology

• John Metzcar, Catherine R Jutzeler, Paul Macklin, Alvaro Köhn-Luque, and Sarah C Brüningk. A review of mechanistic learning in mathematical oncology. Frontiers in Immunology, 15:1363144, 2024.

Frontiers in Neuroscience

• Rosella Trò, Monica Roascio, Domenico Tortora, Mariasavina Severino, Andrea Rossi, Eleftherios Garyfallidis, Gabriele Arnulfo, Marco Massimo Fato, and Shreyas Fadnavis. Multi-view fusion of diffusion MRI microstructural models: a preterm birth study. Frontiers in Neuroscience, 18:1480735, 2024.

Frontiers in Robotics and AI

• Leigh Levinson, Jessica McKinney, Christena Nippert-Eng, Randy Gomez, and Selma Šabanović. Our business, not the robot’s: family conversations about privacy with social robots in the home. Frontiers in Robotics and AI, 11:1331347, 2024.

• Natasha Randall, Swapna Joshi, Waki Kamino, Long-Jing Hsu, Abhijeet Agnihotri, Grace Li, Donald Williamson, Kate Tsui, and Selma Šabanović. Finding ikigai: How robots can support meaning in later life. Frontiers in Robotics and AI, 2022.

• Chaolan Lin, Selma Šabanović, Lynn Dombrowski, Andrew D Miller, Erin Brady, and Karl F MacDorman. Parental acceptance of children’s storytelling robots: A projection of the uncanny valley of AI. Frontiers in Robotics and AI, 8:579993, 2021.

Frontiers in Virtual Reality

• Andreas Bueckle, Kilian Buehling, Patrick C Shih, and Katy Börner. Optimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy Framework. Frontiers in Virtual Reality, 2022.

Genome Research

• Benjamin Giovanni Iovino, Haixu Tang, and Yuzhen Ye. Protein domain embeddings for fast and accurate similarity search. Genome Research, 34(9):1434–1444, 2024.

HKS Misinformation Review

• Dimitar Nikolov, Alessandro Flammini, and Filippo Menczer. Right and left, partisanship predicts (asymmetric) vulnerability to misinformation. HKS Misinformation Review, 2020.

Human Brain Mapping

• Andrew Hannum, Mario A Lopez, Saúl A Blanco, and Richard F Betzel. High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding. Human Brain Mapping, 44(16):5294–5308, 2023.

IEEE Access

• Abualkasim Bakeer, Ihab S Mohamed, Parisa Boodaghi Malidarreh, Intissar Hattabi, and Lantao Liu. An artificial neural network-based model predictive control for three-phase flying capacitor multilevel inverter. IEEE Access, 10:70305–70316, 2022.
• Sherif A Zaid, Ihab S Mohamed, Abualkasim Bakeer, Lantao Liu, Hani Albalawi, Mohamed E Tawfiq, and Ahmed M Kassem. From MPC-based to end-to-end (E2E) learning-based control policy for grid-tied 3L-NPC transformerless inverter. IEEE Access, 10:57309–57326, 2022.

• Md Alimoor Reza, Kai Chen, Akshay Naik, David J. Crandall, and Soon-Heung Jung. Automatic dense annotation for monocular 3d scene understanding. IEEE Access, 8:68852 – 68865, 2020.

IEEE Internet of Things Journal

• Yunji Liang, Sagar Samtani, Bin Guo, and Zhiwen Yu. Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective. IEEE Internet of Things Journal, 7(9):9128–9143, 2020.

IEEE Journal of Selected Topics in Quantum Electronics

• Sadra Rahimi Kari, Carlos A Ríos Ocampo, Lei Jiang, Jiawei Meng, Nicola Peserico, Volker J Sorger, Juejun Hu, and Nathan Youngblood. Optical and electrical memories for analog optical computing. IEEE Journal of Selected Topics in Quantum Electronics, 29(2: Optical Computing):1–12, 2023.

IEEE Journal of Selected Topics in Signal Processing

• Aswin Sivaraman and Minje Kim. Efficient personalized speech enhancement through self-supervised learning. IEEE Journal of Selected Topics in Signal Processing, 16(6):1342–1356, 2022.

IEEE Robotics and Automation Letters (RA-L)

• Alejandro Murillo-González, Junhong Xu, and Lantao Liu. Learning Causal Structure Distributions for Robust Planning. IEEE Robotics and Automation Letters (RA-L), 2025.
• Md Al-Masrur Khan, Durgakant Pushp, and Lantao Liu. AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation. IEEE Robotics and Automation Letters (RA-L), 2025.
• Ihab S Mohamed, Mahmoud Ali, and Lantao Liu. Chance-Constrained Sampling-Based MPC for Collision Avoidance in Uncertain Dynamic Environments. IEEE Robotics and Automation Letters (RA-L), 2025.
• Ruiqi Wang, Dezhong Zhao, Ziqin Yuan, Ike Obi, and Byung-Cheol Min. Prefclm: Enhancing preference-based reinforcement learning with crowdsourced large language models. IEEE Robotics and Automation Letters (RA-L), 2025.

• Ruiqi Wang, Dezhong Zhao, Arjun Gupte, and Byung-Cheol Min. Initial task allocation in multi-human multi-robot teams: An attention-enhanced hierarchical reinforcement learning approach. IEEE Robotics and Automation Letters (RA-L), 9(4):3451–3458, 2024.
• Shyam Sundar Kannan and Byung-Cheol Min. Placeformer: Transformer-based visual place recognition using multi-scale patch selection and fusion. IEEE Robotics and Automation Letters (RA-L), 9(7):6552–6559, 2024.

• Zheng Chen, Zhengming Ding, David J. Crandall, and Lantao Liu. Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation. IEEE Robotics and Automation Letters (RA-L), April 2023.

• Chuhua Wang, Yuchen Wang, Mingze Xu, and David J. Crandall. Stepwise Goal-Driven Networks for Trajectory Prediction. IEEE Robotics and Automation Letters (RA-L), 2022.
• Ihab S Mohamed, Kai Yin, and Lantao Liu. Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy. IEEE Robotics and Automation Letters (RA-L), 2022.

IEEE Signal Processing Letters

• Kai Zhen, Mi Suk Lee, Jongmo Sung, Seungkwon Beack, and Minje Kim. Psychoacoustic calibration of loss functions for efficient end-to-end neural audio coding. IEEE Signal Processing Letters, 27:2159–2163, 2020.

IEEE Systems Journal

• Ying Mao, Yuqi Fu, Wenjia Zheng, Long Cheng, Qingzhi Liu, and Dingwen Tao. Speculative container scheduling for deep learning applications in a kubernetes cluster. IEEE Systems Journal, 2021.

IEEE Transactions on Aerospace and Electronic Systems

• Tingjun Lei, Chaomin Luo, Timothy Sellers, Ying Wang, and Lantao Liu. Multitask allocation framework with spatial dislocation collision avoidance for multiple aerial robots. IEEE Transactions on Aerospace and Electronic Systems, 58(6):5129–5140, 2022.

IEEE Transactions on Affective Computing

• Wonse Jo, Ruiqi Wang, Go-Eum Cha, Su Sun, Revanth Krishna Senthilkumaran, Daniel Foti, and Byung-Cheol Min. MOCAS: A multimodal dataset for objective cognitive workload assessment on simultaneous tasks. IEEE Transactions on Affective Computing, 16(1):116–132, 2024.

IEEE Transactions on Big Data

• Tingyi Wanyan, Akhil Vaid, Jessica K De Freitas, Sulaiman Somani, Riccardo Miotto, Girish N Nadkarni, Ariful Azad, Ying Ding, and Benjamin S Glicksberg. Relational learning improves prediction of mortality in COVID-19 in the intensive care unit. IEEE Transactions on Big Data, 7(1):38–44, 2020.

IEEE Transactions on Cognitive and Developmental Systems

• Ruiqi Wang, Wonse Jo, Dezhong Zhao, Weizheng Wang, Arjun Gupte, Baijian Yang, Guohua Chen, and Byung-Cheol Min. Husformer: A multimodal transformer for multimodal human state recognition. IEEE Transactions on Cognitive and Developmental Systems, 16(4):1374–1390, 2024.

IEEE Transactions on Dependable and Secure Computing

• Sagar Samtani, Hsinchun Chen, Murat Kantarcioglu, and Bhavani Thuraisingham. Explainable Artificial Intelligence for Cyber Threat Intelligence (XAI-CTI). IEEE Transactions on Dependable and Secure Computing, 2022.

IEEE Transactions on Human-Machine Systems

• Wonse Jo, Ruiqi Wang, Baijian Yang, Daniel Foti, Mo Rastgaar, and Byung-Cheol Min. Cognitive load-based affective workload allocation for multihuman multirobot teams. IEEE Transactions on Human-Machine Systems, 2024.

IEEE Transactions on Information Theory

• Changlong Wu, Mohsen Heidari, Ananth Grama, and Wojciech Szpankowski. Regret Bounds for Log-Loss via Bayesian Algorithms. IEEE Transactions on Information Theory, 69(9):5971–5989, 2023.

• Mohsen Heidari, Jithin K Sreedharan, Gil Shamir, and Wojciech Szpankowski. Sufficiently Informative and Relevant Features: An Information-Theoretic and Fourier-Based Characterization. IEEE Transactions on Information Theory, 68(9):6063–6077, 2022.

IEEE Transactions on Neural Networks

• Ryan J Kier, Jeffrey C Ames, Randall D Beer, and Reid R Harrison. Design and implementation of multipattern generators in analog VLSI. IEEE Transactions on Neural Networks, 17(4):1025–1038, 2024.

IEEE Transactions on Parallel and Distributed Systems (TPDS)

• Haoyu Jin, Donglei Wu, Shuyu Zhang, Xiangyu Zou, Sian Jin, Dingwen Tao, Qing Liao, and Wen Xia. Design of a Quantization-based DNN Delta Compression Framework for Model Snapshots and Federated Learning. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2023.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)

• Kristen Grauman, Andrew Westbury, Eugene Byrne, Vincent Cartillier, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Devansh Kukreja, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David J. Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2025.
• Kristen Grauman, Andrew Westbury, and others. Ego4d: Around the world in 3,000 hours of egocentric video. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2025.

• Tianfei Zhou, Fatih Porikli, David J. Crandall, Luc Van Gool, and Wenguan Wang. A Survey on Deep Learning Techniques for Video Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023.

• Satoshi Tsutsui, Yanwei Fu, and David J. Crandall. Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022.
• Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David J. Crandall. DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022.
• Junbo Yin, Jianbing Shen, Xin Gao, David Crandall, and Ruigang Yang. Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022.
• Xiankai Lu, Wenguan Wang, Jianbing Shen, David J. Crandall, and Jiebo Luo. Zero-Shot Video Object Segmentation with Co-Attention Siamese Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 4(4):2228–2242, April 2022.

• Xiankai Lu, Wenguan Wang, Jianbing Shen, David J. Crandall, and Luc Van Gool. Segmenting Objects from Relational Visual Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2021.

IEEE Transactions on Robotics

• Ihab S Mohamed, Junhong Xu, Gaurav S Sukhatme, and Lantao Liu. Towards efficient MPPI trajectory generation with unscented guidance: U-MPPI control strategy. IEEE Transactions on Robotics, 2025.

IEEE/ACM Transactions on Audio, Speech, and Language Processing

• Jinjin Cai, Ruiqi Wang, Dezhong Zhao, Ziqin Yuan, Victoria McKenna, Aaron Friedman, Rachel Foot, Susan Storey, Ryan Boente, Sudip Vhaduri, and others. Multimodal audio-based disease prediction with transformer-based hierarchical fusion network. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2025.

• Sunwoo Kim and Minje Kim. Boosted Locality Sensitive Hashing: Discriminative, Efficient, and Scalable Binary Codes for Source Separation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:2659–2672, 2022.

• Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, and Minje Kim. Scalable and efficient neural speech coding: A hybrid design. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:12–25, 2021.

Information Processing \& Management

• Erkang Jing, Yezheng Liu, Yidong Chai, Jianshan Sun, Sagar Samtani, Yuanchun Jiang, and Yang Qian. A deep interpretable representation learning method for speech emotion recognition. Information Processing & Management, 60(6):103501, 2023.

Information Sciences

• Yunji Liang, Huihui Li, Bin Guo, Zhiwen Yu, Xiaolong Zheng, Sagar Samtani, and Daniel D Zeng. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. Information Sciences, 548:295–312, 2021.

Information Systems Frontiers

• Sagar Samtani, Ziming Zhao, and Ram Krishnan. Secure Knowledge Management and Cybersecurity in the Era of Artificial Intelligence. Information Systems Frontiers, pages 1–5, 2023.

Information Systems Research

• Shuo Yu, Yidong Chai, Sagar Samtani, Hongyan Liu, and Hsinchun Chen. Motion sensor–based fall prevention for senior care: A hidden Markov model with generative adversarial network approach. Information Systems Research, 35(1):1–15, 2024.

Information and Learning Sciences

• Srijita Chakraburty, Krista D Glazewski, Cindy E Hmelo-Silver, Dubravka Svetina Valdivia, Anne Ottenbreit-Leftwich, Bradford Mott, and James Lester. Measuring upper-elementary students’ understanding of AI concepts–a Rasch model analysis. Information and Learning Sciences, 2025.

Intelligence \& robotics

• Tingjun Lei, Guoming Li, Chaomin Luo, Li Zhang, Lantao Liu, and Richard Stephen Gates. An informative planning-based multi-layer robot navigation system as applied in a poultry barn. Intelligence & robotics, 2(4):313–332, 2022.

Intelligent Service Robotics

• Seongcheol Kim, Casey C Bennett, Zachary Henkel, Jinjae Lee, Cedomir Stanojevic, Kenna Baugus, Cindy L Bethel, Jennifer A Piatt, and Selma Šabanović. Generative replay for multi-class modeling of human activities via sensor data from in-home robotic companion pets. Intelligent Service Robotics, 17(2):277–287, 2024.

Interaction Studies

• Marlena R Fraune, Selma Šabanović, and Eliot R Smith. Some are more equal than others: Ingroup robots gain some but not all benefits of team membership. Interaction Studies, 21(3):303–328, 2020.

International Journal of Artificial Intelligence in Education

• Anne Ottenbreit-Leftwich, Krista Glazewski, Minji Jeon, Katie Jantaraweragul, Cindy E Hmelo-Silver, Adam Scribner, Seung Lee, Bradford Mott, and James Lester. Lessons Learned for AI Education with Elementary Students and Teachers. International Journal of Artificial Intelligence in Education, pages 1–23, 2023.

International Journal of Automotive Technology

• Geunsu Kim, Soohyeok Kang, Gyudo Park, and Byung-Cheol Min. Electric vehicle battery state of charge prediction based on graph convolutional network. International Journal of Automotive Technology, 24(6):1519–1530, 2023.

International Journal of Child-Computer Interaction

• Leigh Levinson, Vicky Charisi, Chris Zotos, Randy Gomez, and Selma Šabanović. Let us make robots “Think in child!”: How children conceptualize fairness, inclusion, and privacy with social robots. International Journal of Child-Computer Interaction, 43:100706, 2025.

International Journal of Cognitive and Language Sciences

• Yasmeen Bassas, Sandra Kuebler, and Allen Riddell. Native Language Identification with Cross-Corpus Evaluation Using Social Media Data: 'Reddit'. International Journal of Cognitive and Language Sciences, 17(1):53–57, 2023.

International Journal of Computer Vision (IJCV)

• Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David J. Crandall, and Bo Du. Transformer for Object Re-Identification: A Survey. International Journal of Computer Vision (IJCV), November 2024.

International Journal of Human--Computer Interaction

• Zaiqiao Ye, Zitao Zhang, Xinyao Ma, Eli Blevis, and Selma Sabanovic. Game of life with your companion robot: Exploring the sustainable future for long-term human-robot interaction. International Journal of Human–Computer Interaction, 41(13):8330–8342, 2025.

International Journal of Human-Computer Studies

• Houda Elmimouni, Jennifer A Rode, and Selma Šabanović. Articulation work for supporting the values of students attending class via telepresence robots. International Journal of Human-Computer Studies, 190:103318, 2024.

International Journal of Medical Informatics

• Diane Kuhn, Nicholas Harrison, Paul Musey, David J. Crandall, Peter Pang, Julie Welch, and Christopher Harle. Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments. International Journal of Medical Informatics, March 2025.

International Journal of Research in Marketing

• Keith Carlson, Praveen K Kopalle, Allen Riddell, Daniel Rockmore, and Prasad Vana. Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis. International Journal of Research in Marketing, 2022.

International Journal of Social Robotics

• Swapna Joshi, Waki Kamino, and Selma Šabanović. Social robot accessories for tailoring and appropriation of social robots. International Journal of Social Robotics, 17(5):917–936, 2025.

• Natasha Randall and Selma Šabanović. Designing robots for marketplace success: A case study with technology for behavior and habit change. International Journal of Social Robotics, 16(3):461–487, 2024.

• Waki Kamino, Long-Jing Hsu, Swapna Joshi, Natasha Randall, Abhijeet Agnihotri, Katherine M Tsui, and Selma Šabanović. Making meaning together: co-designing a social robot for older adults with Ikigai experts. International Journal of Social Robotics, 15(6):983–998, 2023.

• Natasha Randall, Selma Šabanović, Staša Milojević, and Apurva Gupta. Top of the class: mining product characteristics associated with crowdfunding success and failure of home robots. International Journal of Social Robotics, 14(1):149–163, 2022.

International Society for Magnetic Resonance Imaging (ISMRM)

• Yixue Feng, Bramsh Qamar Chandio, Tamoghna Chattopadhyay, Sophia I Thomopoulos, Conor Owens-Walton, Neda Jahanshad, Eleftherios Garyfallidis, and Paul M Thompson. Deep generative model for learning tractography streamline embeddings based on convolutional variational autoencoder. International Society for Magnetic Resonance Imaging (ISMRM), 2022.

JMIR Aging

• Cristina Bosco, Ege Otenen, John Osorio Torres, Vivian Nguyen, Darshil Chheda, Xinran Peng, Nenette M Jessup, Anna K Himes, Bianca Cureton, Yvonne Lu, and others. Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study. JMIR Aging, 8:e60566, 2025.

• Natasha Randall, Waki Kamino, Swapna Joshi, Wei-Chu Chen, Long-Jing Hsu, Katherine M Tsui, Selma Šabanović, and others. Understanding the connection among Ikigai, well-being, and home robot acceptance in Japanese older adults: mixed methods study. JMIR Aging, 6(1):e45442, 2023.

JMIR Formative Research

• Fereshtehossadat Shojaei, Fatemehalsadat Shojaei, John Osorio Torres, and Patrick C Shih. Insights from art therapists on using AI-generated art in art therapy: mixed methods study. JMIR Formative Research, 8:e63038, 2024.
• Cristina Bosco, Fereshtehossadat Shojaei, Alec Andrew Theisz, John Osorio Torres, Bianca Cureton, Anna K Himes, Nenette M Jessup, Priscilla A Barnes, Yvonne Lu, Hugh C Hendrie, and others. Testing 3 Modalities (Voice Assistant, Chatbot, and Mobile App) to Assist Older African American and Black Adults in Seeking Information on Alzheimer Disease and Related Dementias: Wizard of Oz Usability Study. JMIR Formative Research, 8(1):e60650, 2024.
• Betsey Zenk Nuseibeh, Shelley A Johns, Patrick C Shih, Gregory F Lewis, Tayler M Gowan, and Evan J Jordan. Co-designing the MOSAIC mHealth app with breast cancer survivors: user-centered design approach. JMIR Formative Research, 8:e59426, 2024.

Journal of Autism and Developmental Disorders

• Daehyoung Lee, Georgia C Frey, Donetta J Cothran, Jaroslaw Harezlak, and Patrick C Shih. Concordance between accelerometer-measured and self-reported physical activity and sedentary time in adults with autism. Journal of Autism and Developmental Disorders, 54(4):1517–1526, 2024.

Journal of Chemical Information and Modeling

• James Michels, Ramya Bandarupalli, Amin Ahangar Akbari, Thai Le, Hong Xiao, Jing Li, and Erik FY Hom. Natural Language Processing Methods for the Study of Protein–Ligand Interactions. Journal of Chemical Information and Modeling, 65(5):2191–2213, 2025.

Journal of Chemical Theory and Computation

• Fanbo Sun, JCS Kadupitiya, and Vikram Jadhao. Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations. Journal of Chemical Theory and Computation, 19(14):4606–4618, 2023.

Journal of Computational Biology

• Yang Wang, Zanyu Shi, Pathum Weerawarna, Kun Huang, Timothy Richardson, and Yijie Wang. Building Explainable Graph Neural Network by Sparse Learning for the Drug–Protein Binding Prediction. Journal of Computational Biology, 2025.

• Wontack Han, Haixu Tang, and Yuzhen Ye. Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype. Journal of Computational Biology, 2022.

Journal of Computational Literary Studies

• Almas Abdibayev, Yohei Igarashi, Allen Riddell, and Daniel Rockmore. Limericks and Computational Poetics: The Minimal Pairs Framework. Computational Challenges for Poetic Analysis and Synthesis. Journal of Computational Literary Studies, 2022.

Journal of Computational Neuroscience

• Abolfazl Alipour, Thomas W James, Joshua W Brown, and Zoran Tiganj. Self-supervised learning of scale-invariant neural representations of space and time. Journal of Computational Neuroscience, 53(1):131–162, 2025.

Journal of Computational Science

• JCS Kadupitiya, Fanbo Sun, Geoffrey Fox, and Vikram Jadhao. Machine learning surrogates for molecular dynamics simulations of soft materials. Journal of Computational Science, 42:101107, 2020.

Journal of Computational Social Science

• Kai-Cheng Yang, Emilio Ferrara, and Filippo Menczer. Botometer 101: Social bot practicum for computational social scientists. Journal of Computational Social Science, pages 1–18, 2022.

Journal of Computational and Graphical Statistics

• John Koo, Minh Tang, and Michael W. Trosset. Popularity Adjusted Block Models are Generalized Random Dot Product Graphs. Journal of Computational and Graphical Statistics, 2022.

Journal of Emergency Management

• Ike Obi, Lojan J. Paul, William Liao, Mariem Loukil, Soichi Hayashi, Max Comer, Carol O. Rogers, David J. Wild, and Patrick C. Shih. Project APRED: A web-based data analytics platform for supporting community disaster resilience. Journal of Emergency Management, 21(5):399–419, 2023.

Journal of Energy Storage

• Selcuk Temiz, Hasan Kurban, Salim Erol, and Mehmet M Dalkilic. Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data. Journal of Energy Storage, 2022.

Journal of Experimental Psychology

• Samantha MW Wood and Justin N Wood. One-shot object parsing in newborn chicks.Journal of Experimental Psychology, 2021.

Journal of Hardware and Systems Security

• Md Alimoor Reza, Zhenhua Chen, and David J. Crandall. Deep Neural Network-based Detection and Verification of Microelectronic Images. Journal of Hardware and Systems Security, 4:44–54, 2020.

Journal of Management Information Systems

• Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, and Hsinchun Chen. Deep learning for information systems research. Journal of Management Information Systems, 2023.

• Hongyi Zhu, Sagar Samtani, Hsinchun Chen, and Jay F Nunamaker Jr. Human identification for activities of daily living: A deep transfer learning approach. Journal of Management Information Systems, 37(2):457–483, 2020.

Journal of Medical Internet Research

• Andy Edinger, Danny Valdez, Eric Walsh-Buhi, Jennifer S Trueblood, Lorenzo Lorenzo-Luaces, Lauren A Rutter, and Johan Bollen. Misinformation and public health messaging in the early stages of the mpox outbreak: mapping the Twitter narrative with deep learning. Journal of Medical Internet Research, 25:e43841, 2023.

Journal of Online Trust and Safety

• Kaicheng Yang, Danishjeet Singh, and Filippo Menczer. Characteristics and Prevalence of Fake Social Media Profiles with AI-generated Faces. Journal of Online Trust and Safety, Sep. 2024.

Journal of Open Source Education

• Bernard t Hart, Titipat Achakulvisut, Ayoade Adeyemi, Athena Akrami, Bradly Alicea, Alicia Alonso-Andres, Diego Alzate-Correa, Arash Ash, Jesus Ballesteros, Aishwarya Balwani, and others. Neuromatch Academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education, 5(49):118, 2022.

Journal of Quantitative Description: Digital Media

• Kai-Cheng Yang and Filippo Menczer. Anatomy of an AI-powered malicious social botnet. Journal of Quantitative Description: Digital Media, 2024.

Journal of Special Education Technology

• Cedomir Stanojevic, Jennifer Piatt, and Selma Sabanovic. Attitudes of special educators towards use of socially assistive robots for individuals with autism Spectrum disorder in Serbia and the United States. Journal of Special Education Technology, 40(3):283–297, 2025.

Journal of Vision

• Philip McAdams, Alexis Colwell, and Linda B Smith. The developmental visual input across environments. Journal of Vision, 25(9):2490–2490, 2025.

• Saber Sheybani, Zoran Tiganj, Justin N Wood, and Linda B Smith. Slow change: An analysis of infant egocentric visual experience. Journal of Vision, 23(9):4685–4685, 2023.
• Lyndon Duong, Kathryn Bonnen, William Broderick, Pierre-Étienne Fiquet, Nikhil Parthasarathy, Thomas Yerxa, Xinyuan Zhao, and Eero Simoncelli. Plenoptic: A platform for synthesizing model-optimized visual stimuli. Journal of Vision, 23(9):5822–5822, 2023.

• Can Oluk, Kathryn Bonnen, Johannes Burge, Lawrence K Cormack, and Wilson S Geisler. Stereo slant discrimination of planar 3D surfaces: Frontoparallel versus planar matching. Journal of Vision, 22(5):6–6, 2022.

Journal of the Association for Information Science and Technology

• Yunxue Cui, Yongzhen Wang, Xiaozhong Liu, Xianwen Wang, and Xuhong Zhang. Multidimensional scholarly citations: Characterizing and understanding scholars' citation behaviors. Journal of the Association for Information Science and Technology, 74(1):115–127, 2023.

Knowledge and Information Systems

• Md Khaledur Rahman, Majedul Haque Sujon, and Ariful Azad. Scalable force-directed graph representation learning and visualization. Knowledge and Information Systems, 64(1):207–233, 2022.

Language Acquisition

• Hadar Karmazyn-Raz and Linda B Smith. Discourse with few words: coherence statistics, parent-infant actions on objects, and object names. Language Acquisition, pages 1–19, 2022.

MIS Quarterly

• Benjamin Ampel, Sagar Samtani, Hongyi Zhu, and Hsinchun Chen. Creating proactive cyber threat intelligence with hacker exploit labels: a deep transfer learning approach. MIS Quarterly, 2024.

• Sagar Samtani, Yidong Chai, and Hsinchun Chen. Linking exploits from the dark web to known vulnerabilities for proactive cyber threat intelligence: An attention-based deep structured semantic model. MIS Quarterly, 46(2):911–946, 2022.
• Mohammadreza Ebrahimi, Yidong Chai, Sagar Samtani, and Hsinchun Chen. Cross-lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning. MIS Quarterly, 2022.

• Hongyi Zhu, Sagar Samtani, Randall Brown, and Hsinchun Chen. A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns. MIS Quarterly, 2020.

Machine Learning: Science and Technology

• JCS Kadupitiya, Geoffrey C Fox, and Vikram Jadhao. Solving Newton's equations of motion with large timesteps using recurrent neural networks based operators. Machine Learning: Science and Technology, 3(2):025002, 2022.

Machine learning

• Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu. Gradient descent optimizes over-parameterized deep ReLU networks. Machine learning, 109(3):467–492, 2020.

Mental Health Science

• Xing Yao, Erik J Nelson, Kostas Stavrianakis, Ting-Yen Huang, Casey Moran, Patrick C Shih, and Evan J Jordan. Where does stress happen? Ecological momentary assessment of daily stressors using a mobile phone app. Mental Health Science, 2(2):e54, 2024.

Nano Letters

• Maha Ibrar, Megan Knobeloch, Nayana Christudas Beena, Yaroslav Losovyj, Sheng-Yuan Huang, Claire McCurtain, David J. Crandall, Stephen Jacobson, and Sara Skrabalak. Stable versus Temporally Sensitive Optical Security Tags from Metal Nanoparticles. Nano Letters, 2025.

Natural Language Engineering

• Hai Hu and Sandra Kübler. Investigating Translated Chinese and Its Variants Using Machine Learning. Natural Language Engineering, 27(3):271–292, 2020.

Natural Language Processing

• He Zhou, Daniel Dakota, and Sandra Kübler. Cross-lingual dependency parsing for a language with a unique script. Natural Language Processing, 31(2):277–305, 2025.

Nature

• Linda B Smith. Can lessons from infants solve the problems of data-greedy AI?Nature, 2024.

• Claudi L Bockting, Eva AM Van Dis, Robert Van Rooij, Willem Zuidema, and Johan Bollen. Living guidelines for generative AI—why scientists must oversee its use. Nature, 622(7984):693–696, 2023.
• Eva AM Van Dis, Johan Bollen, Willem Zuidema, Robert Van Rooij, and Claudi L Bockting. ChatGPT: five priorities for research. Nature, 614(7947):224–226, 2023.

Nature Communications

• Joshua D McGraw, Donsuk Lee, and Justin N Wood. Parallel development of social behavior in biological and artificial fish. Nature Communications, 15(1):10613, 2024.

• Kaiyuan Liu, Yuzhen Ye, Sujun Li, and Haixu Tang. Accurate de novo peptide sequencing using fully convolutional neural networks. Nature Communications, 14(1):7974, 2023.

• Farnaz Zamani Esfahlani, Joshua Faskowitz, Jonah Slack, Bratislav Mišić, and Richard F Betzel. Local structure-function relationships in human brain networks across the lifespan. Nature Communications, 13(1):2053, 2022.

• Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Jie Zhang, Zhengming Ding, and Kun Huang. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nature Communications, 12(1):1–13, 2021.
• Pisanu Buphamalai, Tomislav Kokotovic, Vanja Nagy, and Jörg Menche. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nature Communications, 12(1):6306, 2021.

Nature Human Behaviour

• Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn, Haewoon Kwak, and Jisun An. A semantic embedding space based on large language models for modelling human beliefs. Nature Human Behaviour, pages 1–13, 2025.

• Saumya Bhadani, Shun Yamaya, Alessandro Flammini, Filippo Menczer, Giovanni Luca Ciampaglia, and Brendan Nyhan. Political audience diversity and news reliability in algorithmic ranking. Nature Human Behaviour, 6(4):495–505, 2022.

Nature Machine Intelligence

• Justin N Wood. Artificial intelligence tackles the nature–nurture debate. Nature Machine Intelligence, 6(4):381–382, 2024.
• Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, and others. Factuality challenges in the era of large language models and opportunities for fact-checking. Nature Machine Intelligence, 6(8):852–863, 2024.

• Filippo Menczer, David J. Crandall, Yong-Yeol Ahn, and Apu Kapadia. Addressing the harms of AI-generated inauthentic content. Nature Machine Intelligence, July 2023.

Nature Neuroscience

• Kathryn Bonnen, Thaddeus B Czuba, Jake A Whritner, Adam Kohn, Alexander C Huk, and Lawrence K Cormack. Binocular viewing geometry shapes the neural representation of the dynamic three-dimensional environment. Nature Neuroscience, 23(1):113–121, 2020.

Nature Reviews Physics

• Sibusiso Biyela, Kanta Dihal, Katy Ilonka Gero, Daphne Ippolito, Filippo Menczer, Mike S Schäfer, and Hiromi M Yokoyama. Generative AI and science communication in the physical sciences. Nature Reviews Physics, 6(3):162–165, 2024.

NeuroImage

• Paul A Taylor, Richard C Reynolds, Vince Calhoun, Javier Gonzalez-Castillo, Daniel A Handwerker, Peter A Bandettini, Amanda F Mejia, and Gang Chen. Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility. NeuroImage, 274:120138, 2023.
• Damon Phạm, Daniel J McDonald, Lei Ding, Mary Beth Nebel, and Amanda F Mejia. Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing. NeuroImage, 270:119972, 2023.
• Kurt G Schilling, Shreyas Fadnavis, Joshua Batson, Mereze Visagie, Anna JE Combes, Colin D McKnight, Francesca Bagnato, Eleftherios Garyfallidis, Bennett A Landman, Seth A Smith, and others. Denoising of diffusion mri in the cervical spinal cord–effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity. NeuroImage, 266:119826, 2023.

• Daniel Spencer, Yu Ryan Yue, David Bolin, Sarah Ryan, and Amanda F Mejia. Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. NeuroImage, 249:118908, 2022.
• Amanda F Mejia, Vincent Koppelmans, Laura Jelsone-Swain, Sanjay Kalra, and Robert C Welsh. Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS. NeuroImage, 255:119180, 2022.
• Maria Grazia Puxeddu, Joshua Faskowitz, Olaf Sporns, Laura Astolfi, and Richard F Betzel. Multi-modal and multi-subject modular organization of human brain networks. NeuroImage, 264:119673, 2022.

Neurocomputing

• Vibhas Vats, Md Alimoor Reza, David J. Crandall, and Soon-heung Jung. Blending 3D Geometry and Machine Learning for Multi-View Stereopsis. Neurocomputing, 2025.

• Chuhua Wang, Md Alimoor Reza, Vibhas Vats, Yingnan Ju, Nikhil Thakurdesai, Yuchen Wang, David J. Crandall, Soon-heung Jung, and Jeongil Seo. Deep learning-based 3D reconstruction from multiple images: A survey. Neurocomputing, September 2024.

Neuroinformatics

• Praitayini Kanakaraj, Tianyuan Yao, Leon Y Cai, Ho Hin Lee, Nancy R Newlin, Michael E Kim, Chenyu Gao, Kimberly R Pechman, Derek Archer, Timothy Hohman, and others. Deepn4: learning N4ITK bias field correction for T1-weighted images. Neuroinformatics, 22(2):193–205, 2024.

• Etienne St-Onge, Eleftherios Garyfallidis, and D Louis Collins. Fast streamline search: an exact technique for diffusion MRI tractography. Neuroinformatics, 20(4):1093–1104, 2022.

Nucleic acids research

• Fatemeh Sharifi and Yuzhen Ye. Identification and classification of reverse transcriptases in bacterial genomes and metagenomes. Nucleic acids research, 50(5):e29–e29, 2022.

PLOS Computational Biology

• Lalit Pandey, Donsuk Lee, Samantha MW Wood, and Justin N Wood. Parallel development of object recognition in newborn chicks and deep neural networks. PLOS Computational Biology, 20(12):e1012600, 2024.

• Mohammad Aminul Islam, Michael Getz, Paul Macklin, and Ashlee N Ford Versypt. An agent-based modeling approach for lung fibrosis in response to COVID-19. PLOS Computational Biology, 19(12):e1011741, 2023.

• Jonathan Samir Matthis, Karl S Muller, Kathryn L Bonnen, and Mary M Hayhoe. Retinal optic flow during natural locomotion. PLOS Computational Biology, 18(2):e1009575, 2022.

PLOS One

• Matthew R DeVerna, Rachith Aiyappa, Diogo Pacheco, John Bryden, and Filippo Menczer. Identifying and characterizing superspreaders of low-credibility content on Twitter. PLOS One, 19(5):e0302201, 2024.

• Krishna Bathina, Marijn Ten Thij, and Johan Bollen. Quantifying societal emotional resilience to natural disasters from geo-located social media content. PLOS One, 17(6):e0269315, 2022.

• Andreas Bueckle, Kilian Buehling, Patrick C Shih, and Katy Börner. 3D virtual reality vs. 2D desktop registration user interface comparison. PLOS One, 2021.
• Krishna C Bathina, Marijn Ten Thij, Danny Valdez, Lauren A Rutter, and Johan Bollen. Declining well-being during the COVID-19 pandemic reveals US social inequities. PLOS One, 16(7):e0254114, 2021.

PNAS Nexus

• Morgan R Frank, Yong-Yeol Ahn, and Esteban Moro. AI exposure predicts unemployment risk: A new approach to technology-driven job loss. PNAS Nexus, 4(4):pgaf107, 2025.

• Bao Tran Truong, Xiaodan Lou, Alessandro Flammini, and Filippo Menczer. Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics. PNAS Nexus, 3(7):pgae258, 2024.

Patterns

• Xiaoyi Chen and Haixu Tang. Designing a large language model for chemists. Patterns, 2025.

• Tingyi Wanyan, Hossein Honarvar, Suraj K Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K De Freitas, Ishan Paranjpe, Akhil Vaid, Jing Zhang, and others. Contrastive learning improves critical event prediction in COVID-19 patients. Patterns, 2(12):100389, 2021.

PeerJ Computer Science

• Haewoon Kwak, Jisun An, Elise Jing, and Yong-Yeol Ahn. FrameAxis: characterizing microframe bias and intensity with word embedding. PeerJ Computer Science, 7:e644, 2021.
• Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, and Yong-Yeol Ahn. Persona2vec: a flexible multi-role representations learning framework for graphs. PeerJ Computer Science, 7:e439, 2021.

Personality and Social Psychology Bulletin

• Eliot R Smith, Steven Sherrin, Marlena R Fraune, and Selma Šabanović. Positive emotions, more than anxiety or other negative emotions, predict willingness to interact with robots. Personality and Social Psychology Bulletin, 46(8):1270–1283, 2020.

Philosophical Transactions of the Royal Society B

• Hadar Karmazyn-Raz and Linda B Smith. Sampling statistics are like story creation: a network analysis of parent–toddler exploratory play. Philosophical Transactions of the Royal Society B, 378(1870):20210358, 2023.

Philosophy and the Mind Sciences

• Ezequiel Di Paolo, Evan Thompson, and Randall Beer. Laying down a forking path: Tensions between enaction and the free energy principle. Philosophy and the Mind Sciences, 2022.

Pilot and Feasibility Studies

• Danny Valdez, Kristen N Jozkowski, Katherine Haus, Marijn Ten Thij, Brandon L Crawford, María S Montenegro, Wen-Juo Lo, Ronna C Turner, and Johan Bollen. Assessing rigid modes of thinking in self-declared abortion ideology: natural language processing insights from an online pilot qualitative study on abortion attitudes. Pilot and Feasibility Studies, 8(1):1–14, 2022.

Proceedings of the ACM on Human-Computer Interaction

• Angela Schöpke-Gonzalez, Siqi Wu, Sagar Kumar, and Libby Hemphill. Using off-the-shelf harmful content detection models: Best practices for model reuse. Proceedings of the ACM on Human-Computer Interaction, 9(2):1–27, 2025.
• Forum Modi, Cristina Bosco, Yuxing Wu, and Patrick C Shih. Finding a Place to Belong: Barriers and Solutions for Supporting Trans People of Color on Reddit. Proceedings of the ACM on Human-Computer Interaction, 9(2):1–21, 2025.

• John Osorio Torres, Fereshtehossadat Shojaei, and Patrick C Shih. Classifying technologies during the assessment, treatment planning, documentation and evaluation phases of music therapy: a survey of board-certified practitioners. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2):1–23, 2024.

• Cai Yang, Lexing Xie, and Siqi Wu. The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW2):1–29, 2023.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

• Yunji Liang, Yuchen Qin, Qi Li, Xiaokai Yan, Zhiwen Yu, Bin Guo, Sagar Samtani, and Yanyong Zhang. AccMyrinx: Speech Synthesis with Non-Acoustic Sensor. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(3):1–24, 2022.

Proceedings of the National Academy of Sciences (PNAS)

• Zachary J Petroff, Swapnaa Jayaraman, Linda B Smith, T Rowan Candy, and Kathryn Bonnen. The world through infant eyes: Evidence for the early emergence of the cardinal orientation bias. Proceedings of the National Academy of Sciences (PNAS), 122(16):e2421277122, 2025.

• Matthew R DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, and Filippo Menczer. Fact-checking information from large language models can decrease headline discernment. Proceedings of the National Academy of Sciences (PNAS), 121(50):e2322823121, 2024.

• Stephanie Noble, Amanda F Mejia, Andrew Zalesky, and Dustin Scheinost. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proceedings of the National Academy of Sciences (PNAS), 119(32):e2203020119, 2022.
• Elizabeth M Clerkin and Linda B Smith. Real-world statistics at two timescales and a mechanism for infant learning of object names. Proceedings of the National Academy of Sciences (PNAS), 119(18):e2123239119, 2022.

• Marten Scheffer, Ingrid van de Leemput, Els Weinans, and Johan Bollen. The rise and fall of rationality in language. Proceedings of the National Academy of Sciences (PNAS), 118(51):e2107848118, 2021.
• Johan Bollen, Marijn Ten Thij, Fritz Breithaupt, Alexander TJ Barron, Lauren A Rutter, Lorenzo Lorenzo-Luaces, and Marten Scheffer. Historical language records reveal a surge of cognitive distortions in recent decades. Proceedings of the National Academy of Sciences (PNAS), 118(30):e2102061118, 2021.
• Chen Yu, Yayun Zhang, Lauren K Slone, and Linda B Smith. The infant’s view redefines the problem of referential uncertainty in early word learning. Proceedings of the National Academy of Sciences (PNAS), 118(52):e2107019118, 2021.

• Ian M Bright, Miriam LR Meister, Nathanael A Cruzado, Zoran Tiganj, Elizabeth A Buffalo, and Marc W Howard. A temporal record of the past with a spectrum of time constants in the monkey entorhinal cortex. Proceedings of the National Academy of Sciences (PNAS), 117(33):20274–20283, 2020.

Proceedings of the VLDB Endowment

• Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, and Dingwen Tao. COMET: a novel memory-efficient deep learning training framework by using error-bounded lossy compression. Proceedings of the VLDB Endowment, 2021.

Quantum Science and Technology

• Ruhan Wang, Philip Richerme, and Fan Chen. A hybrid quantum–classical neural network for learning transferable visual representation. Quantum Science and Technology, 8(4):045021, 2023.

Research Ideas and Outcomes

• Sharif Islam, James Beach, Elizabeth R Ellwood, Jose Fortes, Larry Lannom, Gil Nelson, and Beth Plale. Assessing the FAIR digital object framework for global biodiversity research. Research Ideas and Outcomes, 2023.

Science

• Chhandak Bagchi, Filippo Menczer, Jennifer Lundquist, Monideepa Tarafdar, Anthony Paik, and Przemyslaw Grabowicz. Social media algorithms can curb misinformation, but do they?Science, 2024.

Science Advances

• Hao Peng, Qing Ke, Ceren Budak, Daniel M Romero, and Yong-Yeol Ahn. Neural embeddings of scholarly periodicals reveal complex disciplinary organizations. Science Advances, 7(17):eabb9004, 2021.

Science Robotics

• Selma Šabanović, Vicky Charisi, Tony Belpaeme, Cindy L Bethel, Maja Matarić, Robin Murphy, and Shelly Levy-Tzedek. “Robots for good”: Ten defining questions. Science Robotics, 8(84):eadl4238, 2023.

Scientific Reports

• Madhavan KR, Hasan Kurban, Oguzhan M Kulekci, and Mehmet M Dalkilic. Telescope indexing for k-nearest neighbor search algorithms over high dimensional data & large data sets. Scientific Reports, 15(1):24788, 2025.
• Md Taufique Hussain, Mahantesh Halappanavar, Samrat Chatterjee, Filippo Radicchi, Santo Fortunato, and Ariful Azad. Parallel median consensus clustering in complex networks. Scientific Reports, 15(1):3788, 2025.

• Kangsan Lee, Jaehyuk Park, Sam Goree, David J. Crandall, and Yong-Yeol Ahn. Social signals predict contemporary art prices better than visual features, particularly in emerging markets. Scientific Reports, May 2024.
• Mert Onur Cakiroglu, Hasan Kurban, Lilia Aljihmani, Khalid Qaraqe, Goran Petrovski, and Mehmet M Dalkilic. A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph. Scientific Reports, 14(1):31251, 2024.

• Tianyou He, Fritz Breithaupt, Sandra Kübler, and Thomas T Hills. Quantifying the retention of emotions across story retellings. Scientific Reports, 13(1):2448, 2023.
• Harry Yaojun Yan, Kai-Cheng Yang, James Shanahan, and Filippo Menczer. Exposure to social bots amplifies perceptual biases and regulation propensity. Scientific Reports, 13(1):20707, 2023.

• Mohammad R Saeedpour-Parizi, Shirin E Hassan, Ariful Azad, Kelly J Baute, Tayebeh Baniasadi, and John B Shea. Target position and avoidance margin effects on path planning in obstacle avoidance. Scientific Reports, 11(1):1–18, 2021.
• Kathryn Bonnen, Jonathan S Matthis, Agostino Gibaldi, Martin S Banks, Dennis M Levi, and Mary Hayhoe. Binocular vision and the control of foot placement during walking in natural terrain. Scientific Reports, 11(1):20881, 2021.

• Madhavun Candadai and Eduardo J Izquierdo. Sources of predictive information in dynamical neural networks. Scientific Reports, 10(1):1–12, 2020.

Scientometrics

• Haining Wang, Jason Clark, Hannah McKelvey, Leila Sterman, Zheng Gao, Zuoyu Tian, Sandra Kübler, and Xiaozhong Liu. Science out of its Ivory Tower: improving accessibility with reinforcement learning. Scientometrics, pages 1–25, 2025.

Sensors

• Tingjun Lei, Pradeep Chintam, Chaomin Luo, Lantao Liu, and Gene Eu Jan. A convex optimization approach to multi-robot task allocation and path planning. Sensors, 23(11):5103, 2023.

Society for Computation in Linguistics

• Damir Cavar, Zoran Tiganj, Ludovic Veta Mompelat, and Billy Dickson. Computing Ellipsis Constructions: Comparing Classical NLP and LLM Approaches. Society for Computation in Linguistics, 2024.

Sociological Methods \& Research

• Bernard J Koch, Tim Sainburg, Pablo Geraldo Bastias, Song Jiang, Yizhou Sun, and Jacob G Foster. A Primer on Deep Learning for Causal Inference. Sociological Methods & Research, 54(2):397–447, 2025.

• Alina Arseniev-Koehler and Jacob G Foster. Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat. Sociological Methods & Research, 51(4):1484–1539, 2022.

Studies in Digital Heritage

• Laura Loredana Micoli, Umair Shafqat Malik, and Gabriele Guidi. Virtual Reconstruction of no longer Existing Archaeological Structures in Highly Urbanized Areas. Studies in Digital Heritage, 8(1):36–66, 2024.

The Annals of Applied Statistics

• Daniel J McDonald, Michael McBride, Yupeng Gu, and Christopher Raphael. Markov-switching state space models for uncovering musical interpretation. The Annals of Applied Statistics, 15(3):1147–1170, 2021.

The International Journal of High Performance Computing Applications

• JCS Kadupitiya, Geoffrey C Fox, and Vikram Jadhao. Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles. The International Journal of High Performance Computing Applications, 34(3):357–374, 2020.

The International Journal of Robotics Research

• Junhong Xu, Kai Yin, Jason M Gregory, Kris Hauser, and Lantao Liu. Boundary-aware value function generation for safe stochastic motion planning. The International Journal of Robotics Research, 43(12):1936–1958, 2024.
• Junhong Xu, Kai Yin, Zheng Chen, Jason M Gregory, Ethan A Stump, and Lantao Liu. Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains. The International Journal of Robotics Research, 43(7):1056–1080, 2024.
• Weizhe Chen, Roni Khardon, and Lantao Liu. Adaptive robotic information gathering via non-stationary Gaussian processes. The International Journal of Robotics Research, 43(4):405–436, 2024.

Topics in Cognitive Science

• Randall D Beer. On the proper treatment of dynamics in cognitive science. Topics in Cognitive Science, 2023.

• Eeshan Hasan, Quentin Eichbaum, Adam C Seegmiller, Charles Stratton, and Jennifer S Trueblood. Improving Medical Image Decision-Making by Leveraging Metacognitive Processes and Representational Similarity. Topics in Cognitive Science, 14(2):400–413, 2022.

Transactions on Social Computing

• Yelena Mejova, Jisun An, Gianmarco De Francisci Morales, and Haewoon Kwak. Modeling political activism around gun debate via social media. Transactions on Social Computing, 5(1-4):1–28, 2022.

Trends in Cognitive Sciences

• Linda B Smith and Hadar Karmazyn-Raz. Episodes of experience and generative intelligence. Trends in Cognitive Sciences, 2022.

• Tara van Viegen, Athena Akrami, Kathryn Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W Lindsay, Patrick Mineault, John D Murray, Xaq Pitkow, and others. Neuromatch Academy: Teaching computational neuroscience with global accessibility. Trends in Cognitive Sciences, 25(7):535–538, 2021.

Tribology Letters

• JCS Kadupitiya and Vikram Jadhao. Probing the rheological properties of liquids under conditions of elastohydrodynamic lubrication using simulations and machine learning. Tribology Letters, 69(3):82, 2021.

XRDS: Crossroads

• Long-Jing Hsu, Waki Kamino, Weslie Khoo, Katherine Tsui, David J. Crandall, and Selma Sabanovic. Working Together Toward ikigai: Co-Designing Robots That Can Help Us Achieve Meaning and Purpose in Life. XRDS: Crossroads, 30(1):38–45, 2023.

npj Digital Medicine

• R Laubenbacher, A Niarakis, G Helikar, G An, B Shapiro, R.S. Malik-Sheriff, T.J. Sego, A. Knapp, Paul Macklin, and J.A. Glazier. Building digital twins of the human immune system: toward a roadmap. npj Digital Medicine, 5(1):64, 2022.

npj Quantum Information

• Mohammad Aamir Sohail, Mohsen Heidari, and S Sandeep Pradhan. Quantum natural stochastic pairwise coordinate descent. npj Quantum Information, 11(1):109, 2025.

npj Systems Biology and Applications

• Heber L. Rocha, Boris Aguilar, Michael Getz, Ilya Shmulevich, and Paul Macklin. A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins. npj Systems Biology and Applications, 10(1):144, 2024.
• Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L Fonseca, James Glazier, Tomas Helikar, Marti Jett-Tilton, Denise Kirschner, and others. Forum on immune digital twins: a meeting report. npj Systems Biology and Applications, 10(1):19, 2024.

Workshop papers

AAAI International Workshop on Practical Deep Learning in the Wild

• Vibhas Vats and David J. Crandall. Controlling the Quality of Distillation in Response-Based Network Compression. In AAAI International Workshop on Practical Deep Learning in the Wild, 2022.

AAAI Workshop on Neuro-Symbolic Learning and Reasoning in the Era of Large Language Models

• David Leake, Zachary Wilkerson, and David J. Crandall. Combining Case-Based Reasoning with Deep Learning: Context and Ongoing Case Feature Learning Research. In AAAI Workshop on Neuro-Symbolic Learning and Reasoning in the Era of Large Language Models, 2024.

Arabic Natural Language Processing Workshop (WANLP)

• Noor Abo Mokh, Daniel Dakota, and Sandra Kübler. Improving POS Tagging for Arabic Dialects on Out-of-Domain Texts. In Arabic Natural Language Processing Workshop (WANLP), 2022.

IEEE CVPR Joint International Third Ego4D and Eleventh EPIC Workshop

• Sam Goree and David Crandall. Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision. In IEEE CVPR Joint International Third Ego4D and Eleventh EPIC Workshop, 2023.

IEEE Security and Privacy Workshops (SPW)

• Yang Gao, Benjamin Ampel, and Sagar Samtani. Examining the Robustness of Machine Learning-Based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming. In IEEE Security and Privacy Workshops (SPW), 288–293, 2025.
• Adhishree Kathikar, Ben Lazarine, Yang Gao, Ankit Shah, and Sagar Samtani. Generating Secure Artificial Intelligence Model Source Code: A Reinforcement Learning Approach. In IEEE Security and Privacy Workshops (SPW), 265–269, 2025.

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

• Darius Petermann, Seungkwon Beack, and Minje Kim. Harp-net: Hyper-autoencoded reconstruction propagation for scalable neural audio coding. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 316–320, 2021.
• Aswin Sivaraman and Minje Kim. Zero-Shot Personalized Speech Enhancement Through Speaker-Informed Model Selection. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 171–175, 2021.
• Sunwoo Kim and Minje Kim. Test-time adaptation toward personalized speech enhancement: Zero-shot learning with knowledge distillation. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 176–180, 2021.

IEEE/ACM Workshop on Education for High-Performance Computing (EduHPC)

• Vikram Jadhao and JCS Kadupitiya. Integrating machine learning with hpc-driven simulations for enhanced student learning. In IEEE/ACM Workshop on Education for High-Performance Computing (EduHPC), 25–34, 2020.

International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

• Zhenxiao Fu and Fan Chen. Quantum neural network extraction attack via split co-teaching. In International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 1–5, 2025.

International Conference on Case-based Reasoning Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems

• Ziwei Zhao, David Leake, Xiaomeng Ye, and David J. Crandall. Generating Counterfactual Images: Toward a C2C-VAE Approach. In International Conference on Case-based Reasoning Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems, 2022.

International Workshop on Machine Learning and Data Mining for Sports Analytics

• Anshumaan Shankar, Gowtham Veerabadran Rajasekaran, Jacob Hendricks, Jared Andrew Schlak, Parichit Sharma, Madhavan KR, Hasan Kurban, and Mehmet M Dalkilic. Are Sports Awards About Sports? Using AI to Find the Answer. In International Workshop on Machine Learning and Data Mining for Sports Analytics, 91–102, 2023.

International Workshop on Treebanks and Linguistic Theories (TLT)

• Kenneth Steimel, Akbar Amat, Arienne Dwyer, and Sandra Kübler. Fine-Grained Morpho-Syntactic Analysis for the Under-Resourced Language Chaghatay. In International Workshop on Treebanks and Linguistic Theories (TLT), 2020.

Intrinsically-Motivated and Open-Ended Learning Workshop@ NeurIPS2023

• Donsuk Lee, Samantha Wood, and Justin Wood. Imprinting in autonomous artificial agents using deep reinforcement learning. In Intrinsically-Motivated and Open-Ended Learning Workshop@ NeurIPS2023, 2023.

SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

• Zuoyu Tian and Sandra Kübler. Period Classification in Chinese Historical Texts. In SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, 2021.

Safe Generative AI Workshop

• Ruhan Wang and Dongruo Zhou. Safe decision transformer with learning-based constraints. In Safe Generative AI Workshop, volume 283, 1–27, 2025.

Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR)

• David Leake. Case-Based Explanation: Making the Implicit Explicit. In Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR), 2022.

• Lawrence Gates and David Leake. Evaluating CBR Explanation Capabilities: Survey and Next Steps. In Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR), 40–51, 2021.

Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

• Yue Chen, Yingnan Ju, and Sandra Kübler. IUCL at WASSA 2022 Shared Task: A Text-Only Approach to Empathy and Emotion Detection. In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 228–232, 2022.

Workshop on Online Abuse and Harms (WOAH)

• Dante Razo and Sandra Kübler. Investigating Sampling Bias in Abusive Language Detection. In Workshop on Online Abuse and Harms (WOAH), 70–78, 2020.