Qiang Sun
Visiting Associate Professor of Statistics and Data Science
Research Interests
Professor Sun is broadly interested in statistics + AI, with a focus on leveraging statistics to make AI reliable and trustworthy. Motivated by challenges in the industrial sector, Sun’s research extends to ensemble learning, transfer learning, GenAI, and reinforcement learning. On the application front, Sun explores AI’s potential in technology and science domains, such as algorithmic trading, user growth strategies, material science and engineering. Advocating for problem- and data-centric approaches in statistics and AI, he aims to drive tangible advancements for societal benefit.
On the application front, Sun explores AI’s potential in technology and science domains, such as algorithmic trading, user growth strategies, material science and engineering. Advocating for problem- and data-centric approaches in statistics and AI, he aims to drive tangible advancements for societal benefit.
Prior to joining MBZUAI, Professor Sun was associate research scholar at Princeton University from 2014–2017. He is currently associate professorship of statistics at the University of Toronto. At Toronto, he leads the StatsLE (Statistics, Learning, and Engineering) group, exploring various topics spanning statistics and AI. Professor Sun earned his Ph.D. at the University of North Carolina at Chapel Hill in 2014, and his B.Sc from the University of Science and Technology of China in 2010.
Professor Sun also serves as an associate editor for The Electronic Journal of Statistics (EJS), and an area chair for UAI, AISTATS, and COLT. In collaboration with colleagues, he is actively involved in establishing an open community on Stats + AI, providing a learning, collaborative, and outreach platform for young professionals in statistics and AI.
- Ph.D. in statistics from the University of North Carolina at Chapel Hill
- Bachelor of Science in SCGY from the University of Science and Technology of China
- James E. Grizzle Distinguished Alumni at UNC-CH, 2022.
- Noah’s Ark Distinguished Lecture at Montreal, QC, 2020.
- Discovery Launch Supplement Award, NSERC, 2019
- Connaught New Researcher Award, University of Toronto, 2018
Chairing/executive positions
- Associate Editor for the Electronic Journal of Statistics (2024- )
- Area Chair for the Conference on Learning Theory (COLT) (2021-2024)
- Area Chair for Artificial Intelligence and Statistics (AIStats) (2022-2024)
- Area Chair for the Conference on Uncertainty in Artificial Intelligence (UAI) (2023)
- Fang XH, Li J, Sun Q, and Wang BY. “Rethinking the uniformity metric in self-supervised learning”. ICLR, 2024.
- Yang R, Yang YL, Zhou F, and Sun Q. “Directional diffusion models for graph representation learning”. NeurIPS, 2023.
- Chen X, Zeng YC, Yang SY, and Sun Q (2023). Sketched ridgeless linear regression: The role of downsampling, ICML 2023.
- Zhai Z, Chen HC, and Sun Q. “Quadratic matrix factorization with applications to manifold learning”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
- Jiang B, Sun Q, and Fan J. “Bayesian factor–adjusted sparse regression”. Journal of Econometrics, 230, 3–19, 2022.
- Fan J, Jiang B, and Sun Q. “Hoeffding’s lemma for general Markov chains with applications to statistical learning”. Journal of Machine Learning Research, 22, 1–35, 2021.