I’m a first-year Ph.D. student in the Machine Learning Department at CMU, advised by Ameet Talwalkar. My current research focuses on automating machine learning in real-world applications, in particular democratizing model learning and tuning for diverse tasks outside the widely-studied vision and language domains. Besides, I’m also interested in studying theories of explainable machine learning.
I obtained my B.S. in Mathematics of Computation at UCLA, where I was fortunate to work with Dr. Lin Yang on sample-efficient reinforcement learning. I also studied multi-agent RL and Theory of Mind, advised by Professor Song-Chun Zhu and Professor Ying Nian Wu.
09/2021: Our work on Iterative Teacher-Aware Learning is accepted by NeurIPS 2021!
05/2021: I'll be joining CMU this Fall as a Machine Learning Ph.D. student!
01/2021: Mathematical Reconstruction of Patient-Specific Vascular Networks is accepted for publication!
12/2020: Our work on accelerating Deep RL with nearest neighbor function approximation is accepted by AAAI 2021! Check out the code and slides.
Doctor of Philosophy, Machine Learning
Carnegie Mellon University, 2021 - present
B.S. in Mathematics of Computation (GPA 4.0/4.0)
University of California, Los Angeles, 2017 - 2021
Experimenting with the FRAME and Hierarchical FRAME model.
Learning how to find goals and avoid obstacles within a maze.