Harshal D. Kaushik

Harshal Kaushik is a Research Faculty at the NSF AI Institute for Advances in Optimization (AI4OPT), Georgia Tech. With more than eight years of experience, he specializes in operations research, game theory, and data analytics. His current research focuses on supply chains, logistics, and semiconductor manufacturing through collaborations with industry partners.

Previously, at UT Dallas, Dr. Kaushik collaborated with the National Labs and industry partners on projects involving power systems, EV infrastructure planning, and sustainable transportation. His doctoral research focused on the development and analysis of first-order algorithms for large-scale distributed optimization problems.

Dr. Kaushik has also served as a Postdoctoral Associate in the Department of Electrical and Computer Engineering at Virginia Tech and as a Data Scientist at Bayer.

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hkaushik8@gatech.edu

Professional Experience

Research Faculty

NSF AI Institute for Advances in Optimization (AI4OPT), Georgia Tech, GA (March. 2026 - Present)

Postdoctoral Research Associate

Design and Optimization of Energy Systems (DOES) Laboratory, UT Dallas, TX (Oct. 2023 - Dec. 2025)

Data Scientist

Bayer Corporation, St. Luise, MO (Jul. 2022 - Aug. 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!

2026

January: A manuscript submitted to the ASME Journal of Mechanical Design.
February: Accepted a position as Research Faculty with the NSF AI Institute for Advances in Optimization (AI4OPT), Georgia Tech.
July: A paper was accepted at 2026 IEEE Power & Energy Society General Meeting (PESGM).

2025

November: A paper submitted to IEEE PES General Meeting 2026.
October: Attended INFORMS Annual Meeting in Atlanta, Georgia.
September: A paper published in Cell Reports iScience.
August: Served as a Program Committee Member at the IEEE ITEC-AP 2025, Singapore.
August: Presented my research on hybrid fuel cell-powered aircraft (Cessna 208) at the 2025 IDETC-CIE in Anaheim, California.
July: Presented my research on EV infrastructure at the 2025 IEEE Power & Energy Society General Meeting (PESGM) in Austin, TX.
June: A paper accepted in Cell Reports iScience.
May: A paper accepted in the IEEE Transactions on Transportation Electrification.
May: Three papers accepted to ASME IDETC-CIE conference.
March: Three papers submitted to ASME IDETC-CIE conference.
March: A manuscript submitted to Cell Reports iScience.
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"