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.
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]
BnB-PEP: A Unified Methodology for Constructing Optimal Optimization Methods
ORC Student Seminar, April 2022
NExOS.jl
for Nonconvex Exterior-point Optimization [Youtube video]
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
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.
5.00/5.00
[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
NExOS.jl
[github repo]
Optimization Models for Energy-efficient Railway Timetables. (Grade: A+) [pdf] [html presentation]