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Padhraic Smyth

Distinguished Professor of Computer Science and Director of the Data Science Initiative

Padhraic Smyth studies machine learning, artificial intelligence, pattern recognition and applied statistics.

Areas of Expertise

  • Machine Learning
  • Artificial Intelligence
  • Pattern Recognition
  • Statistics

Biography

Padhraic Smyth holds the Hasso Plattner Endowed Chair in Artificial Intelligence and is a Distinguished Professor in the Department of Computer Science at UC Irvine. He also has joint faculty appointments in the Department of Statistics and in the Department of Education. His research interests include machine learning, artificial intelligence, pattern recognition, and applied statistics and he has published over 200 papers on these topics. He is an ACM Fellow, IEEE Fellow, AAAI Fellow and AAAS Fellow, and was a recipient of the ACM SIGKDD Innovation Award. He is co-author of the text Modeling the Internet and the Web: Probabilistic Methods and Algorithms (Wiley, 2003) and Principles of Data Mining (MIT Press, 2001). He served as program chair of the ACM SIGKDD 2011 and UAI 2013 conferences, associate program chair for IJCAI 2022, general chair for AI-Stats 1997, and in various senior/area chair positions for conferences such as NeurIPS, ICML, and AAAI. He has also served in editorial and advisory positions for journals such as the Journal of Machine Learning Research, the Journal of the American Statistical Association, and the IEEE Transactions on Knowledge and Data Engineering.

Padhraic was the founding director of the UCI Center for Machine Learning and Intelligent Systems from 2007 to 2014 and founding director from 2014 to 2018 of the UCI Data Science Initiative. While at UC Irvine he has received research funding from agencies such as NSF, NIH, IARPA, NASA, NIST, ONR, and DOE, and from companies such as Google, Qualcomm, Microsoft, eBay, Adobe, IBM, SAP, Xerox, and Experian. In addition to his academic research he is also active in industry consulting, working with companies such as Toshiba, Samsung, Oracle, Nokia, and AT&T, as well as serving as scientific advisor to local startups in Orange County. He also served as an academic advisor to Netflix for the Netflix prize competition from 2006 to 2009.

Padhraic grew up in Kilmovee, County Mayo, in the west of Ireland and received a first class honors degree in Electronic Engineering from National University of Ireland (University of Galway) in 1984, and the MSEE and PhD degrees (in 1985 and 1988 respectively) in Electrical Engineering from the California Institute of Technology. From 1988 to 1996 he was a researcher at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996.

Media

Watch on YouTube: Spotlight On Science - Padhraic Smyth, PhD - Artificial Intelligence: A New Era for Science?Watch on YouTube: PSML Nexus - 5/25/21 - Professor Padhraic Smyth, Computer Science,  UC IrvineWatch on YouTube: Padhraic Smyth - Data Science Initiative 2014Download image: Padhraic SmythDownload image: Padhraic Smyth

Education

California Institute of Technology

Ph.D., Electrical Engineering, 1988

California Institute of Technology

MSEE, Electrical Engineering, 1985

National University of Ireland

BE, Engineering (Electronic), 1984

Accomplishments

  • Outstanding Paper with Lead Student Author, International Conference on AI and Statistics
  • Qualcomm Faculty Award
  • Fellow, Institute for Electrical and Electronic Engineers (IEEE)
  • Fellow, American Association for the Advancement of Science (AAAS)
  • Best Paper, Educational Data Mining Conference (EDM)

Patents

Hidden Markov Models for Fault Detection in Dynamic Systems,

U.S. Patent no. 5465321

Cross-Connect Switch and Method for Providing Test Access Thereto,

U.S. Patent no. 4845736

Cross-Connect Switch

U.S. Patent no. 4807280

Affiliations

  • AAAI President’s Fellows Advisory Board : Member, 2019–present

Research Grants

CAIG: Advancing Wildfire Science, Prediction, and Management with Machine Learning

NSF 2425932, 10/2024-9/2027

Time Series Prediction with Deep Learning

Google, 9/2024

Individualized Learning and Prediction for Heterogeneous Multimodal Data from Wearable Devices

NIH R01 CA297869-01,, 7/2024-6/2028

AI/ML and Data Science Training Datasets

NIH 3OT2OD032581,, 1/2023-3/2024

Improving Prediction of Fire Extremes in the GEOS Forecasting System on Daily and Seasonal Timescales,

NASA, 9/2021-6/2025

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