Hey!

I’m a Ph.D. student at Columbia University, where I think about machine learning with the amazing Hongseok Namkoong. Particularly, I’m interested in scaling RL environments for LLM, teaching models to solve long-horizon ambiguous tasks (\(\neg\) RLVR), continual learning, and the associated memory methodology.

I obtained a Master’s in Machine Learning at Carnegie Mellon University. I had a great time studying distribution shift and machine learning at large, all thanks to working with incredible people like Saurabh Garg and Zachary Lipton.

Previously, I was a physics enthusiast at University of Toronto, who failed to understand Quantum Field Theory (QFT) and General Relativity (GR). While failing, I was fortunate enough to work with the inspiring Artur Izmaylov on quantum computing. I’ve postponed understanding QFT and GR, but please catch and teach me if you do.

Outside of productive hours, I enjoy spending time with friends :)

Contact

I’m always open to collaborations and conversations! If you’d like to chat, feel free to reach out by email (see top left).

If you’re a motivated undergraduate or master’s student interested in doing exciting ML research, please fill out this form join Hong’s Lab.