Andrew (Ching-Yuan) Bai

Hello! Welcome to my personal website.

I am a 4th year PhD student in Computer Science at UCLA, where I work with Cho-Jui Hsieh. My research interests involve understanding the memorization and forgetting of machine learning models and mechanisms that control them, with more recent focus on large language models. My most recent research topic of focus is characterizing the instruction-tuning techniques that are best for downstream preference alignment (RLHF) performance in LLMs. I also engage in concurrent collaboration projects spanning multiple topics, including LLM agents, forgetting in instruction-tuning, multi-modal model (MLLM) interpretability, diffusion model data memorization, and prompt optimization.

Previously I proposed a simple and easy-to-adopt sample selection schemes for prioritizing data samples during training. I also worked on developing practical interpretation methods for black-box models, allowing humans to better understand and trust machine learning models in real life (concept-based interpretability).

I was an undergraduate student in Computer Science at National Taiwan University. I worked with Hsuan-Tien Lin on generative modeling and time series forecasting. We held the first-ever generative modeling competition in collaboration with Kaggle. I also worked with Chung-Wei Lin on system verification and falsification.

Email: andrewbai [AT] cs.ucla.edu

Links: [CV] [Github] [Google Scholar] [Linkedin]

Publications

2024

2023

2022

2021

2020