About me

I’m Hung, a first-year PhD student at Princeton ORFE, where I am honored to be supported by the Gordon Wu Fellowship. I am currently interested in the theoretical foundations of deep learning broadly interpreted, as inspired by Misha Belkin and Becca Willett, as well as my own (painful) experiences of applying it.

I previously graduated from the University of Chicago with a B.S. in Mathematics, where I mainly spent my time on probability theory and reinforcement learning. During this time I was fortunate to learn from and work with Ewain Gwynne, Matt Walter, Yuxin Chen, Shiry Ginosar, and Kaylene Stocking.

I am also interested in the theoretical foundations of RL, with a view towards applications in robotics.

My email is tchle [at] princeton [dot] edu.

I am currently trying to play jazz on the piano and to write a chuckleatable stand-up routine.

Updates

• 8/2026 I'll be participating in the Princeton Machine Learning Theory Summer School!
• 6/2026 I graduated from UChicago!

Papers

• 2/2025 Active Advantage-Aligned Online Reinforcement Learning with Offline Data Xuefeng Liu, Hung T. C. Le, Siyu Chen, Rick Stevens, Zhuoran Yang, Matthew R. Walter, Yuxin Chen Exploration in AI Today (EXAIT) Workshop at ICML 2025 arXiv

Expositions

• 11/2025 Behavior of Infinitely Wide Neural Networks For Amie Wilkinson and Danny Calegari's MATH 35401 Introduction to Machine Learning pdf
• 3/2025 Strong Law of Large Numbers and Kingman's Subadditive Ergodic Theorem For Aaron Calderon's MATH 27600 Dynamical Systems pdf
• 8/2024 Risk-averse Dynamic Programming for Tree-based Controlled Markov Decision Processes Cole Franklin*, Denisse Garnica*, Hung T. C. Le*, Benjamin May* Fields Undergraduate Summer Research Program 2024 pdf
• 6/2024 Elliptic Functions and Plane Cubics For Aaron Calderon's MATH 27000 Complex Analysis pdf
• 8/2023 Harmony in Randomness: the Laplacian and the Heat Equation UChicago REU Apprentice 2023 pdf