Jubayer Ibn Hamid

I am an incoming PhD student at Stanford University. My research is currently advised by Chelsea Finn and Dorsa Sadigh. I studied Mathematical Physics (B.S) and Computer Science (M.S), both at Stanford University.

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

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I am currently interested in deep exploration methods for online reinforcement learning fine-tuning, spanning both language models and embodied agents. My research also focuses on test-time decoding from complex, multimodal policies and training robotic policies with long context. Additionally, I am interested in understanding what kinds of data to scale for improved generalization in robot learning.

• 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

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.

• 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.

Talks

Bidirectional Decoding. OpenAI. 25th February, 2025.

Teaching

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

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