Shuvomoy Das Gupta

About Me

Hello world! I am a third year Ph.D. student at the Operations Research Center at MIT, advised by Professor Bart Van Parys.

I obtained my M.A.Sc. (GPA 4.00/4.00) from the ECE Department, University of Toronto in 2016. After that, I worked as a researcher at the Research & Technology Department of Thales Canada for more than two and a half years. My M.A.Sc. research work on optimization models to compute energy-efficient railway timetables has been integrated with the largest installed base of communication based train control systems worldwide. Previously, I obtained my B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology.


Peer-reviewed Papers


  • NExOS.jl for Nonconvex Exterior-point Operator Splitting [Youtube video]

  • Exterior-point Operator Splitting for Nonconvex Learning [Slides]

    • Seoul National University, November 17, 2020 (talk given to Professor Ernest Ryu's research group)

    • INFORMS Annual Meeting, November 10, 2020

    • MIT LIDS student conference, January 2021

  • On seeking efficient Pareto optimal points in multi-player minimum cost flow problems [Slides]

    • MIT LIDS student conference, January 2020

  • On Convergence of Heuristics Based on Douglas-Rachford Splitting and ADMM to Minimize Convex Functions over Nonconvex Sets [Slides]

    • Allerton Conference on Communication, Control, and Computing, October 2018

  • Multi-player minimum cost flow problems with nonconvex costs and integer flows [Slides]

    • IEEE Conference on Decision and Control, December 2016

  • An Optimization Model to Utilize Regenerative Braking Energy in a Railway Network [Slides]

    • American Control Conference, July 2015


  • Optimization Methods (Fall 2020)

    • Teaching assistant for a course that aims to provide masters students with a unified overview of the main algorithms and areas of application in optimization.

    • Duties: Assisting students, leading recitations, writing and marking assignments and exams.

Cumulative GPA


Courses taken

  • [6.251] Introduction to Mathematical Programming

  • [6.436] Fundamentals of Probability

  • [6.252] Nonlinear Optimization

  • [6.881] Optimization for Machine Learning

  • [9.521] Mathematical Statistics

  • [6.860] Statistical Learning Theory

  • [18.456] Algebraic Techniques and Semidefinite Optimization


MASc Thesis

Optimization Models for Energy-efficient Railway Timetables. (Grade: A+) [pdf] [html presentation]