Shuvomoy Das Gupta

Shuvomoy Das Gupta
Ph.D. Student
Operations Research Center
MIT
Email: sdgupta@mit.edu
Google Scholar

About Me

Hello world! I am a third year Ph.D. student at the Operations Research Center at MIT co-advised by Professor Robert M. Freund and Professor Andy Sun.

My research focuses on developing efficient algorithms for large-scale optimization and machine learning. I have recently worked on BnB-PEP, which is the first unified methodology to construct optimal first-order algorithms for convex and nonconvex optimization.

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 almost three 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.

Papers

• Shuvomoy Das Gupta, Bart P.G. Van Parys, and Ernest K. Ryu, “Branch-and-Bound Performance Estimation Programming: A Unified Methodology for Constructing Optimal Optimization Methods”, 2022. [pdf] [code]

• Shuvomoy Das Gupta, Bartolomeo Stellato, and Bart P.G. Van Parys, “Exterior-point Optimization for Nonconvex Learning”, 2021. [pdf] [NExOS.jl Julia package] [INFORMS 2020 Presentation]

• Shuvomoy Das Gupta and Lacra Pavel, “On seeking efficient Pareto optimal points in multi-player minimum cost flow problems with application to transportation systems”, in the Journal of Global Optimization 74 (2019): 523-548. [pdf] [presentation]

• Shuvomoy Das Gupta, “On Convergence of Heuristics Based on Douglas-Rachford Splitting and ADMM to Minimize Convex Functions over Nonconvex Sets”, in the proceedings of the 56th Allerton Conference on Communication, Control, and Computing, University of Illinois at Urbana-Champaign, IL, USA, October 2018. [pdf] [presentation]

• Shuvomoy Das Gupta and Lacra Pavel, “Multi-player minimum cost flow problems with nonconvex costs and integer flows”, in the proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, USA, December 12-14, 2016. [pdf] [longer version with proofs] [presentation]

• Shuvomoy Das Gupta, J. Kevin Tobin, and Lacra Pavel, “A two-step linear programming model for energy-efficient timetables in metro railway networks”, in Transportation Research Part B: Methodological 93 (2016): 57–74. [pdf]

• Shuvomoy Das Gupta, Lacra Pavel, and J. Kevin Tobin, “An Optimization Model to Utilize Regenerative Braking Energy in a Railway Network”, in the proceedings of 2015 American Control Conference (ACC), Chicago, IL, USA, July 2015. [pdf] [presentation]

Talks

• BnB-PEP: A Unified Methodology for Constructing Optimal Optimization Methods

  • ORC Student Seminar, April 2022

  • MIT Data Science Lab Seminar, June 10, 2022

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

  • Juliacon 2021

• Exterior-point Optimization 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

Teaching

• 15.066: System Optimization (Summer 2022)

• 15.S08: Optimization of Energy Systems (Spring 2022)

  • Teaching assistant for a course that aims to aims to lead PhD or advanced master students to the forefront of energy system research.

  • Duties: Developing Julia implementations of next generation power system models, assisting students, leading recitations, writing and marking assignments and exams.

• 15.S60: Computing in Optimization and Statistics (2022)

  • Instructor for the advanced optimization session. [Jupyter notebook 1] [Jupyter notebook 2] [Student feedback]

• 15.093: 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

5.00/5.00

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

Software

• NExOS.jl [github repo]

MASc Thesis

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