Research
"Keep being coupled to other people, keep spreading your ideas, because the sum of all of us together, coupled, is greater than our own parts." - Hasson U, 2016
Research Interests
We are generally interested in statistics + AI, broadly defined. More recently, driven by challenges in the industry sectors, we are interested in
Online and reinforcement learning;
Transfer learning: Fine/prompt tuning, causal transfer learning, and model merging ;
Reliable/trustworthy AI: Ensemble learning, the role of randomization, algorithmic adaptivity, robustness to adversarial and random noises;
Generative AI: Diffusion models and 3D generation;
AI for tech, finance, and science: Algorithmic trading, imaging sciences, material science and engineering.
Reading Seminar
We are runnning reading seminars from
Fall 2022 and Winter 2023: Learning + X (Joint with Shi and Zhou)
Winter and Summer 2022: RL + X (Joint with Shi, Yang and Zhou)
Fall 2021: RMT4ML
Winter 2021: RSL and SL
Fall 2020: RA and RL
Fall 2018: High Dimensional Inference (Joint with Reid).
Winter 2018: Statistical Machine Learning (Joint with Kong)
Reading seminar tips (from Stanford ML group)