Padhraic Smyth

About

Padhraic Smyth is a distinguished professor and the associate director at the Center for Machine Learning and Intelligent Systems at the University of California, Irvine. He has been appointed the inaugural Hasso Plattner Endowed Chair in Artificial Intelligence, recognizing his significant contributions to the field of AI. His career has been marked by the development of theories and algorithms for machine learning, with a particular emphasis on statistical methods that have advanced the discipline. Smyth's work has earned him notable recognition, including the Best Paper Award at AIStats 2026 for his collaboration on developing a deep generative model for forecasting temporal point processes. His research and leadership continue to influence the evolution of machine learning and artificial intelligence.

Research topics

• Computer Science
• Data science
• World Wide Web
• Data Mining
• Artificial Intelligence
• Human–computer interaction
• Geography
• Earth science
• Ecology
• Oceanography

Selected publications

• Mining Big Data in Education: Affordances and Challenges

  Review of Research in Education · 2020 · 389 citations

  The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesol…

  DOI

• Zonally contrasting shifts of the tropical rainbelt in response to climate change.

  Nature Climate Change · 2021 · 168 citations

  Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ re…

  DOI

• What large language models know and what people think they know

  Nature Machine Intelligence · 2025-01-21 · 107 citations

  articleOpen accessSenior author

  Abstract As artificial intelligence systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and communicate the likelihood of their predictions being correct. Whereas recent work has focused on LLMs’ internal confidence, less is understood about how effectively they convey uncertainty to users. Here we exp…

  PublisherOA PDFDOI

• The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

  International Journal of Educational Technology in Higher Education · 2020 · 95 citations

  Senior authorCorresponding

  Abstract Student clickstream data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analyti…

  DOI

• Systematically tracking the hourly progression of large wildfires using GOES satellite observations

  Earth system science data · 2024-03-15 · 14 citations

  articleOpen accessCorresponding

  Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develo…

  PublisherOA PDFDOI

Recent grants

• RI: Medium: Assessment of Machine Learning Algorithms in the Wild

  NSF · $1.2M · 2019–2025

• Personalized Risk Predictions with Deep Learning Methods in the Presence of Missing and Biased Electronic Health Record Data

  NIH · $1.3M · 2021–2026

• Data Mining of Digital Behaviour

  NSF · $2.2M · 2001–2010

Frequent coauthors

• Yang Chen

  Nanyang Technological University

  200 shared

• Mark Steyvers

  35 shared

• Efi Foufoula‐Georgiou

  Irvine University

  35 shared

• James T. Randerson

  University of California, Irvine

  31 shared

• Usama M. Fayyad

  Northeastern University

  25 shared

• R.M. Goodman

  24 shared

• Alexander Ihler

  University of California, Irvine

  22 shared

• Heikki Mannila

  Aalto University

  21 shared

Education

• PhD, Electrical Engineering

  California Institute of Technology

  1988

• Bachelor of Engineering, Electronic Engineering

  National University of Ireland (NUIG)

  1984

Awards & honors

• Hasso Plattner Endowed Chair in Artificial Intelligence
• 2023 INNS Dennis Gabor Award

Similar researchers at University of California, Irvine

• Pierre Baldih-121
• Gene Tsudikh-82
• Sven Koenigh-75