Han Zhao

Han Zhao
Han Zhao
  • Assistant Professor
3320 Thomas M. Siebel Center for Computer Science

For More Information

Education

  • Bachelor of Engineering, Computer Science, Tsinghua University, 2013
  • Master of Mathematics, David R. Cheriton School of Computer Science, University of Waterloo, 2015
  • Doctor of Philosophy, Machine Learning Department, School of Computer Science, Carnegie Mellon University, 2020

Academic Positions

  • Long-Term Visitor, Modern Paradigms in Generalization, Simons Institute for the Theory of Computing, August 2024-December 2024
  • Tenure-Track Assistant Professor, Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, August 2021-Present
  • Affiliated Assistant Professor, Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, August 2021-Present

Research Interests

  • Neuro-symbolic AI
  • Trustworthy Machine Learning
  • Post-training & Alignment of Large Language Models

Research Topics

  • AI Algorithms
  • AI Theory

Other Outside Service

  • Amazon Scholar, Amazon, May 2022 - Present

Honors

  • Dean's Award for Excellence in Research, 2026
  • Outstanding Paper Award, EMNLP, 2025 (2025)
  • NSF CAREER Award, 2025 (2025)
  • Google Research Scholar Award, 2024 (2024)
  • AAAI New Faculty Highlights, 2024 (2024 )
  • Kavli Fellow, National Academy of Sciences, 2023 (2023)
  • Teacher Ranked as Excellent, University of Illinois Urbana Champaign, Fall 2021 (Fall 2021 )
  • Facebook Research Award (Statistics for Improving Insights, Models, and Decisions), 2021 (2021 )
  • Alumni Gold Medal Award, University of Waterloo, 2015 (2015)
  • Distinguished Graduate of Tsinghua University, 2013 (2013)
  • Liu Jimin Scholarship, Tsinghua University, 2012 (2012)
  • Google Excellence Scholarship, 2012 (2012)

Recent Courses Taught

  • CS 442 - Trustworthy Machine Learning
  • CS 446 (ECE 449) - Machine Learning
  • CS 498 ML3 (CS 498 MLG, CS 498 MLU) - Trustworthy ML
  • CS 591 MLR - Machine Learning Reading Grp
  • CS 591 MLR - ML Reading Group
  • CS 598 FDS - Foundations of Data Science
  • CS 598 HAZ (CS 598 HOF) - Transfer Learning