I am a 1st year PhD student at Stanford University. I work in artificial intelligence with a focus on algorithms for sequential decision making and reinforcement learning.
My research is advised by Chelsea Finn and Dorsa Sadigh. I am affiliated with Stanford Artificial Intelligence Laboratory (SAIL).
Previously, I studied mathematical physics as an undergraduate at Stanford University. Outside of AI, I am interested in pure mathematics, especially abstract algebra and neighbouring fields. I am interested in the connections of other seemingly disparate fields of science to our understanding of machine learning.
Selected Publications
-
Polychromic Objectives for Reinforcement Learning. Jubayer Ibn Hamid*, Ifdita Hasan Orney*, Ellen Xu, Chelsea Finn, Dorsa Sadigh. Preprint. -
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling. Yuejiang Liu*, Jubayer Ibn Hamid*, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn. International Conference on Learning Representations (ICLR), 2025. (Website)
(*) denotes co-first authorship
Full list of publications can be found here: Publications.
Notes
Introductory notes on various topics I have found to be fascinating and/or useful in my own research.
-
Category Theory and Algebraic Geometry. Notes on introductory category theory and foundational constructions in algebraic geometry (work in progress).
-
Abstract Algebra. Notes on abstract algebra (work in progress).
-
Whitney's Embedding Theorem and Immersion Theorem. Weak versions of Whitney's Embedding and Immersion theorem, which are often sufficient.
-
Policy Gradient Methods. Building blocks of policy gradient methods in online reinforcement learning.
-
Trust Region Optimization Methods. Introduction to trust region methods for optimization.
Teaching
CS 224R - Deep Reinforcement Learning. Head CA. Spring, 2025.
CS 229 - Machine Learning. CA. Winter, 2025.