Jubayer Ibn Hamid

I am an incoming PhD student at Stanford University. Previously, I studied mathematical physics as an undergraduate at Stanford University.

I work in artificial intelligence at the intersection of reinforcement learning and generative models. I am also interested in pure mathematics such as abstract algebra and category theory.

My research is advised by Chelsea Finn and Dorsa Sadigh in Stanford Artificial Intelligence Laboratory (SAIL).

I am a diehard Barca fan. I was born and raised in the beautiful city of Dhaka in Bangladesh.

• Strategic Exploration Learning. Jubayer Ibn Hamid*, Ayush Chakravarthy*, Anikait Singh*, Yoonho Lee*, Chelsea Finn. In progress.

• 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)

• Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning. Kyle Hsu*, Jubayer Ibn Hamid*, Kaylee Burns, Chelsea Finn, Jiajun Wu. International Conference on Machine Learning (ICML), 2024.

• What Makes Pre-trained Visual Representations Successful For Robust Manipulation? Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid, Tianhe Yu, Chelsea Finn, Karol Hausman. Conference on Robot Learning (CoRL), 2024.

(*) denotes co-first authorship

These are introductory notes on various topics that have fascinated me that cover some of the basic constructions that show up periodically.

• Category Theory and Algebraic Geometry. Notes on introductory category theory and foundational constructions in algebraic geometry (work in progress).

• Algebraic Topology. Foundational constructions – fundamental group, homology and cohomology. (Incomplete, will typeset later).

• 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.