Sayantan Choudhury
Postdoctoral Researcher at MBZUAI
Welcome to my homepage. I am currently a Postdoctoral Researcher at MBZUAI, hosted by Martin Takac and Mladen Kolar. I completed my Ph.D. in Applied Mathematics and Statistics at Johns Hopkins University, where I was very fortunate to work under the supervision of Nicolas Loizou.
I am broadly interested in the mathematical foundations of machine learning, with a particular focus on optimization methods for modern large-scale learning problems.
Before joining Johns Hopkins, I obtained my B.Stat and M.Stat from the Indian Statistical Institute, Kolkata. During Fall 2023, I was a research intern at MBZUAI, where I worked on adaptive methods for modern deep learning.
Contact: sayantan.choudhury4 [at] gmail [dot] com
News
Last Updated: May 13, 2026.
| 2026 | Two new preprints are now available on arXiv: |
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| 2026 | Our paper One-shot Conditional Sampling: MMD meets Nearest Neighbors was accepted at ICML 2026. |
| 2025 | I started as a Postdoctoral Researcher at MBZUAI in November 2025. |
| 2025 | Excited that two papers were accepted at NeurIPS 2025: |
| 2025 | I completed my Ph.D. in Applied Mathematics and Statistics at Johns Hopkins University and received the Chen Family Dissertation Fellowship for Spring 2025. |
| 2025 | Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity was accepted at ICLR 2025. |
Selected Publications
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ICLR 2025Accepted at ICLR, 2025.
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ICML 2026
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Preprint 2026
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NeurIPS 2025Accepted at NeurIPS, 2025.
Education
- 2025: Ph.D. in Applied Mathematics and Statistics, Johns Hopkins University
- 2023: MSE in Applied Mathematics and Statistics, Johns Hopkins University
- 2020: M.Stat, Indian Statistical Institute, Kolkata
- 2018: B.Stat, Indian Statistical Institute, Kolkata
Research Interests
- Large-Scale Optimization
- Adaptive Optimization Algorithms
- Federated Learning
- Min-Max Optimization
- Distributed and Decentralized Algorithms
- Generative Models