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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)

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

JANET: Joint adaptive prediction-region estimation for time-series

What large language models know and what people think they know

A generative diffusion model for probabilistic ensembles of precipitation maps conditioned on multisensor satellite observations

Dynamic conditional optimal transport through simulation-free flows

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Modeling the Internet and the Web: Probabilistic Methods and Algorithms

Advances in Knowledge Discovery and Data Mining

Principles of Data Mining (Adaptive Computation and Machine Learning)

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.

Machine Learning

Artificial Intelligence

Pattern Recognition

Statistics

UCI Faculty Profile

Google Scholar Citations

Wikipedia Page

How UCI and AI go waaay back

A call to hit the ‘pause’ button on AI experiments

Bayesian consensus prediction for correlated human experts and classifiers

Understanding gender bias in AI-generated product description

ELBOing Stein: Variational Bayes with Stein mixture inference

Benchmark data repositories for better benchmarking

Hidden Markov Models for Fault Detection in Dynamic Systems,

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

Cross-Connect Switch

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

Time Series Prediction with Deep Learning

Individualized Learning and Prediction for Heterogeneous Multimodal Data from Wearable Devices

AI/ML and Data Science Training Datasets

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

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

Newport Beach Public Library Foundation presents Spotlight on Science 

Padhraic Smyth, PhD "Artificial Intelligence - A New Era for Science?" Thursday, November 15 , 2024 

www.nbplf.foundation

Spotlight On Science - Padhraic Smyth, PhD - Artificial Intelligence: A New Era for Science?

Title:  Deep Learning and Statistics: Connections

Abstract: Deep learning techniques have received widespread attention given their impressive empirical performance over a range of pattern recognition problems. These problems include applications in computer science, such as computer vision, text processing, and speech analysis, as well as (more recently) data-driven problems in the natural sciences. Much of this work has occurred outside the context of statistics - yet, many of the key ideas underlying deep learning have statistical foundations. In this talk we will explore deep learning from a statistical perspective. We will discuss some of the key concepts in deep learning and how these concepts are related to well-known ideas in statistical modeling and estimation. We will also highlight key differences, both technically and culturally, in terms of how data analysis problems are approached by researchers in both fields. The talk is intended to be tutorial in nature and accessible to an audience with relatively little knowledge about either deep learning or statistics.

PSML Nexus - 5/25/21 - Professor Padhraic Smyth, Computer Science,  UC Irvine

Data Science Initiative
Oct. 24, 2014

"What is the UC Irvine Data Science Initiative?"
by Padhraic Smyth

Padhraic Smyth - Data Science Initiative 2014

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