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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Research
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.
Publications:
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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)
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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.
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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
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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.
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Talks
Bidirectional Decoding. OpenAI. 25th February, 2025.
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