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Learning is key for any type of intelligence. I love thinking about how human learns, and whether that is transferable to machines. Questions like
Why ML requires more data than human?intrigue me.
My research interests are broadly learning robust user representations, and aligning machine to human reasoning. I’m fortunate to be working with Prof. Sarah Dean, Prof. Wen Sun, and Prof. Thorsten Joachims at Cornell. Currently, I study reinforcement learning and human-in-loop dynamical systems. Sample-efficient low-rank representation learning for dynamical and unobserved user states, computationally-efficient weighted logistic bandits, and “algorithm-of-thoughts” styled personalized question-understanding system are examples of what I am working on.
- Representation Learning in Low-rank Slate-based Recommender Systems2023
- Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration2023
- Growth Mechanisms and the Effects of Deposition Parameters on the Structure and Properties of High Entropy Film by Magnetron SputteringMaterials, 2019