Chen-Hao Chao
Ph.D. in CS @ University of Toronto
Hello, I'm a Computer Science Ph.D. student at the University of Toronto (UofT), advised by Prof. Rahul G. Krishnan. I am honored to be a recipient of the Mary H. Beatty Fellowship. I completed my master's and bachelor's degrees in Computer Science at National Tsing Hua University (NTHU). I collaborated on research projects with Prof. Chun-Yi Lee, visited Prof. Zsolt Kira's lab at Georgia Tech, and interned at NVIDIA and MediaTek.
My current research explores efficient pre-training methods and scaling behavior of diffusion language models. More broadly, my works cover probabilistic modeling and generative AI, spanning both discrete and continuous generative methods (e.g., score-based and flow-based models) with applications to reinforcement learning, visual domain adaptation, and biological data visualization. I built:
My current research explores efficient pre-training methods and scaling behavior of diffusion language models. More broadly, my works cover probabilistic modeling and generative AI, spanning both discrete and continuous generative methods (e.g., score-based and flow-based models) with applications to reinforcement learning, visual domain adaptation, and biological data visualization. I built:
- MDM-Prime (v1, v2): A scalable diffusion language model that allows partial word editings.
- EBFlow/MEow: A flow-based model that enables energy-based reinterpretation and RL applications.
- QCSBM: An architecturally flexible diffusion model that satisfies the conservative property.
- DLSM: A classifier-guidance diffusion model enhanced by likelihood score matching.
latest posts [full list]
selected publications [full list]
- TPAMI
Rainbow UDA: Combining Domain Adaptive Models for Semantic Segmentation TasksIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
