Jubayer Ibn Hamid

I am an incoming Ph.D. student, currently working at Stanford Artificial Intelligence Laboratory (SAIL) where I am advised by Chelsea Finn. I studied Mathematical Physics (B.S) and Computer Science (M.S) at Stanford University.

I work in artificial intelligence with a focus on the intersection of reinforcement learning and generative models. I am also interested in pure mathematics such as abstract algebra and algebraic geometry.

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

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Research

My current research interests in robotics lie in test-time inference methods for robotic policies and scalable online reinforcement learning fine-tuning. I am also interested in generally understanding what kinds of data we should scale for better generalization.

(*) denotes co-first authorship

  • Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling. Yuejiang Liu*, Jubayer Ibn Hamid*, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn. International Conference on Learning Representations (ICLR), 2025. (Paper, Website, Blog)

  • 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. (Paper)

  • 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. (Paper)

Notes

Here are some introductory notes on various topics that have fascinated me. These are not meant to be in-depth. Rather, they are meant to cover some of the basic constructions that show up periodically and are also interesting in and of themselves.

  • Algebraic Geometry. Foundational constructions and results in algebraic geometry, with some category theory and algebra review for completeness(Incomplete).

  • 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 (including the policy gradient theorems for both episodic and continuing tasks) of policy gradient algorithms.

Teaching

CS 224R - Deep Reinforcement Learning : Head CA. Spring, 2025.

CS 229 - Machine Learning : CA. Winter, 2025.


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