I am Harshal, currently employed as a Postdoctoral Research Associate at the Design and Optimization of Energy Systems (DOES) Laboratory, UT Dallas. With over eight years of experience, I bring expertise in nonlinear programming, algorithm development, game theory, machine learning, and data analytics, with a strong emphasis on their real-world applications. In my current role at UTD, I serve both as an individual contributor and a key collaborator (with Idaho National Laboratory, University at Buffalo, and UT Austin) on projects focused on power grids and energy systems, EV infrastructure planning, and the design of all-electric ships.
I completed my Ph.D. in 2021 under the guidance of Dr. Farzad Yousefian, where my research was primarily focused on the development and analysis of first-order algorithms designed for large-scale distributed optimization problems, with applications in game theory and machine learning.
In the past, I held positions as a Postdoctoral Associate in the Department of Electrical and Computer Engineering at Virginia Tech and as an Operations Research Data Scientist at Bayer Crop Science. Apart from work, I enjoy travel, music, Cricket, and swimming.
harshal.kaushik[at]utdallas.edu
Professional Experience
Postdoctoral Research Associate
Design and Optimization of Energy Systems (DOES) Laboratory, UT Dallas, TX (Nov. 2023 - present)
Data Scientist
Bayer Corporation, St. Luise, MO (Jul. 2022 - Jul. 2023)
Postdoctoral Associate
Bradley Department of Electrical and Computer Engineering, Virginia Tech, VA (Oct. 2021 - Jul. 2022)
Graduate Research Assistant
School of Industrial Engineering and Management, Oklahoma State University, OK (Jan. 2017 - Sep. 2021)
Operations Research Intern
Schneider, Green Bay, WI (May 2019 - Aug. 2019)
Project Associate
Indian Intitute of Technology (IIT) Madras (Jun. 2015 - Nov. 2016)
News and Updates!
2025
March: Three papers submitted to ASME IDETC-CIE conference.
March: A manuscript submitted to Cell Reports Sustainability.
February: A paper accepted to IEEE PES General Meeting 2025.
2024
December: A proposal draft submitted to Nature Reviews.
November: A paper submitted to IEEE Transactions on Transportation Electrification.
November: A paper submitted to IEEE PES General Meeting 2025.
November: A paper submitted to IEEE Transactions on Power Systems.
October: A paper submitted at the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025).
April: Two papers accepted at the 2024 IEEE KPEC conference: https://arxiv.org/abs/2404.13422, https://arxiv.org/abs/2404.14452
March: Submitted two articles at the 2024 IEEE KPEC conference.
2023
October: Co-chaired a session at the INFORMS Annual Meeting 2023 "Predictive Analytics and Game Theoretic Techniques for Smart Network Grids" with Prof. Ming Jin.
October: Accepted a Postdoctoral Research Associate position at the Design and Optimization of Energy Systems (DOES) Laboratory, UT Dallas.
August: Ended my tenure with Bayer Crop Science as an Operations Research Data Scientist.
April: Article accepted in IEEE Transactions on Automatic Control "An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints".
March: Paper accepted for IFAC WC 2023 "Decision-Focused Learning for Inverse Noncooperative Games: Generalization Bounds and Convergence Analysis".
January: Article accepted in AAAI-23 "On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds".
2022
December: Submitted an article "An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints".
November: Submitted an article "Decision-Focused Learning for Inverse Noncooperative Games: Generalization Bounds and Convergence Analysis".
October: Session chair at the INFORMS Annual Meeting "Predictive Analytics for Game Theory".
October: Session chair at the INFORMS Annual Meeting "Trustworthy Reinforcement Learning for Energy Systems".
July: Started working as a Data Scientist at Bayer Corporation.
May: Submitted an article "On Solution Functions of Optimization: Universal Approximation and Complexity Analysis"
April: Guest lecture for Advanced Machine Learning Course, ECE 5424 - Virginia Tech "Decision-Focused Learning and Improved Generalization Bounds for Noncooperative Games"
April: Poster presentation at the CPES-PEC Conference, Virginia Tech "Decision-focused Utility Learning"
March: Submitted an article "Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints"
2021
December: Graduated from the School of Industrial Engineering and Management, Oklahoma State University.
October: Talk at the INFORMS Annual Meeting "Distributed optimization problems with variational inequality constraints: algorithms, complexity analysis, and applications"
October: Session chair at the INFORMS Annual Meeting "Distributed Optimization Methods for Hierarchical Problems"
October: Started working as a Postdoctoral Associate at The Bradley Department of Electrical and Computer Engineering
October: Submitted an article "Distributed Randomized Block Stochastic Gradient Tracking Method"
August: Defended Ph.D. thesis "Distributed optimization problems with variational inequality constraints: algorithms, complexity analysis, and applications"
June: Article accepted in the SIAM Journal on Optimization "A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints"
May: Awarded "Graduate College Robberson Summer Research and Creative Activity Grant" from the CEAT, Oklahoma State University
May: Talk at the American Control Conference (ACC) "An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization"
January: Article accepted at the American Control Conference (ACC) "An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization"
2020
December: IEM Doctoral Student Award (for exemplary performance in the Industrial Engineering and Management program).
November: Talk at the INFORMS Annual Meeting "An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization"
November: Submitted an article "An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization"
October: Awarded Roy and Virginia Dorrough Distinguished Graduate Fellowship
March: Article accepted in the International Journal of Reliability and Safety "A Log-third Order Polynomial Normal Transformation Approach for High-reliability Estimation with Scarce Samples"
2019
October: Talk at the INFORMS Annual Meeting "First-order Methods for Optimization Over the Solution Set of Variational Inequality Problems"
May: Started working as an Operations Research Intern at Schneider National
May: Talk at the American Control Conference (ACC) "A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization"
January: Article accepted at the American Control Conference (ACC) "A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization"
2018
November: Talk at the INFORMS Annual Meeting "A First Order Method for High-dimensional Ill-posed Optimization Problems"
November: Submitted an article "A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization"