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Padhraic Smyth

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Distinguished Professor
Department of Computer Science (vice-chair)
Department of Statistics
School of Information and Computer Sciences
University of California, Irvine

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Research Interests: machine learning, artificial intelligence, pattern recognition, statistics

Affiliations (at UCI)

• Associate (and Founding) Director, Center for Machine Learning and Intelligent Systems
• Faculty Director, UCI Machine Learning Repository
• Co-Director, HPI Research Center in Machine Learning and Data Science at UCI
• Faculty Affiliate, UCI Steckler Center for Responsible, Ethical, and Accessible Technology (CREATe)
• Faculty Affiliate, UCI Connected Learning Lab (CLL)

Selected Recent Papers

• Perceptions of linguistic uncertainty by language models and humans
  with Catarina Belem, Markelle Kelly, Mark Steyvers, and Sameer Singh, Proceedings of the 2024 Empirical Methods in Natural Language Processing Conference (EMNLP), 2024
• Functional flow matching
  with Gavin Kerrigan and Giosue Migliorini, 27th International Conference on AI and Statistics (Outstanding Student Paper Award), 2024
• Bayesian online learning for consensus prediction
  with Sam Showalter, Alex Boyd, and Mark Steyvers, 27th International Conference on AI and Statistics , 2024
• Capturing humans mental models of AI: an item response theory approach
  with Markelle Kelly, Aakriti Kumar, and Mark Steyvers, ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2023
• Probabilistic querying of continuous-time event sequences
  with Alex Boyd, Yuxin Chang, and Stephan Mandt, 26th International Conference on AI and Statistics, 2023
• Climate-driven changes in the predictability of seasonal precipitation
  with P. Le, J. T. Randerson, R. Willett, S. Wright, C. Guilloteau, A. Mamalakis, E. Foufoula-Georgiou, Nature Communications, 14:3822, 2023
• Predictive querying for autoregressive neural sequence models
  with Alex Boyd, Sam Showalter, Stephan Mandt, Neural Information Processing Systems 35 (NeurIPS 2022)
• Fair generalized linear models with a convex penalty
  with Hyungrok Do, Preston Putzel, Axel Martin, and Judy Zhong, International Conference on Machine Learning (ICML), 2022
• Bayesian modeling of human-AI complementarity
  with Mark Steyvers, Heliodoro Tejeda, and Gavin Kerrigan, Proceedings of the National Academy of Sciences, 2022

Teaching

• CS 274A, Probabilistic Learning
• CS 175, Project in Artificial Intelligence
• CS 178, Machine Learning and Data Mining (link available to UCI students via Canvas)
• Stats 5, Seminar in Data Science
• Stats 170A/B, Capstone Project in Data Science