Sayantan Choudhury

Hi! I am Sayantan Choudhury, a 4th year PhD candidate in the Department of Applied Mathematics and Statistics at Johns Hopkins University. I am very fortunate to work under the supervision of Nicolas Loizou.

My research is in the area of Optimization and Machine Learning. My work analyses Extragradient Methods for solving structured non-convex problems and Federated Learning. I am also interested in adaptive methods and methods for solving large-scale linear equations. In Fall 2023, I was a research intern at MBZUAI, UAE, where I was working with Martin Takac on designing adaptive methods.

Before coming to JHU, I completed my B.Stat and M.Stat from Indian Statistical Institute, Kolkata. During my Bachelors and Masters, I was involved in several research projects at the Indian Statistical Institute and the Centre for Science of Student Learning. After joining JHU, I completed my MSE in Applied Mathematics and Statistics with a concentration on Optimization.

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  • 2025 (Expected) - Ph.D. in Applied Math & Stat, Johns Hopkins University
  • 2023 - MSE in Applied Math & Stat, Johns Hopkins University
  • 2020 - Master's in Statistics, Indian Statistical Institute, Kolkata
  • 2018 - Bachelor's in Statistics, Indian Statistical Institute, Kolkata

Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horvath, Martin Takac, Eduard Gorbunov.


Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Siqi Zhang*, Sayantan Choudhury*, Sebastian Stich, Nicolas Loizou.
* Equal Contribution.
Accepted at ICLR, 2024.


Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou.
Accepted at NeurIPS, 2023.



I have worked as a Teaching Assistant for the following courses:

  • Spring 2023 - Iterative Algorithms in Machine Learning: Theory and Applications, Johns Hopkins University.
  • Spring 2023 - Optimization in Data Science, Johns Hopkins University.
  • Fall 2022 - Large Scale Optimization for Data Science, Johns Hopkins University.
  • Spring 2022 - Machine Learning II, Johns Hopkins University.
  • Fall 2021 - Introduction to Convexity, Johns Hopkins University.
  • Spring 2021 - Network Analysis and Operations Research, Johns Hopkins University.

Selected Honors & Awards

  • 2023 - Acheson J. Duncan Fund for the Advancement of Research in Statistics.
  • 2023 - NeurIPS 2023 Scholar Award.
  • 2022 - MINDS Fellowship.
  • 2019 - Award for Excellent Academic Performance in Masters First Year at Indian Statistical Institute, Kolkata.
  • 2015 - KVPY Fellowship, 5-year scholarship for Bachelors and Masters.
  • 2015 - Selected for INSPIRE Fellowship.

Invited Talks & Posters

  • 2023 - Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA (Poster)
  • 2023 - SIAM Conference on Optimization (OP23), Seattle, USA (Talk)
  • 2023 - Annual Conference on Information Sciences and Systems (CISS 2023), Baltimore, USA (Talk)

Thanks to Jon Barron for the website's template.