Devansh Jalota, Assistant Professor in Georgia Tech ISyE

Jerry and Harriet Thuesen Early Career Professor and
Assistant Professor

Contact

 George Tower 1019
 Personal website
 Curriculum Vitae
  Contact
  • Devansh Jalota LinkedIn
  • Devansh Jalota Google Scholar

Education

  • Ph.D. Computational and Applied Mathematics (2025), Stanford University
  • B.S. Applied Mathematics, Civil and Environmental Engineering (2019), University of California, Berkeley

About

Devansh Jalota is the Jerry and Harriet Thuesen Early Career Professor and an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research lies at the intersection of operations research, economics, and computer science, with a focus on socially responsible market design for socio-technical systems, including transportation and electricity markets. Prior to joining Georgia Tech, he was a Postdoctoral Research Scientist at Columbia University’s Data Science Institute. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University, where he was a Thomas C. Nelson Stanford Interdisciplinary Graduate Fellow, and his B.S. in Civil and Environmental Engineering and B.A. in Applied Mathematics from the University of California, Berkeley.

Research

Devansh's research develops data-driven learning algorithms and incentive mechanisms for the design of socially responsible markets and resource allocation systems, with a focus on transportation and electricity markets. A central theme of his work is understanding how uncertainty, privacy, accessibility, strategic behavior, and operational constraints shape both market outcomes and the algorithms used to implement them. Methodologically, his work draws on optimization, market design, game theory, and online learning. His recent and ongoing research explores pricing and allocation mechanisms in transportation systems, including congestion pricing, road-user charging, and informal transit. He is also beginning to study questions at the intersection of electricity markets and the rapidly growing power needs of AI infrastructure, including how large new loads may interact with grid capacity, investment incentives, and market design. Across these areas, his goal is to develop analytically rigorous and practically relevant mechanisms that better align individual incentives with broader system objectives such as efficiency, resilience, accessibility, and sustainability.

Awards and Honors

  • 2025 INFORMS JFIG Best Paper Competition, Finalist
  • 2024 INFORMS TSL Best Student Paper Award, Finalist
  • Thomas C. Nelson Stanford Interdisciplinary Graduate Fellow
  • TOTAL Innovation Scholar
  • Best Presentation, WINE 2020
  • Departmental Citation, Civil and Environmental Engineering, UC Berkeley

Representative Publications

  • Devansh Jalota, Michael Ostrovsky, and Marco Pavone. “Matching with Transfers under Distributional Constraints.” Games and Economic Behavior, 152: 313–332, 2025.
  • Devansh Jalota and Yinyu Ye. “Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements.” Operations Research, 73(2): 798–818, 2025.
  • Damien Berriaud, Ezzat Elokda, Devansh Jalota, Emilio Frazzoli, Marco Pavone, and Florian Dörfler. “To Spend or to Gain: Online Learning in Repeated Karma Auctions.” Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 289–297, 2025.
  • Devansh Jalota, Kiril Solovey, Karthik Gopalakrishnan, Stephen Zoepf, Hamsa Balakrishnan, and Marco Pavone. “When Efficiency Meets Equity in Congestion Pricing and Revenue Refunding Schemes.” IEEE Transactions on Control of Network Systems, 11(2): 1127–1138, 2024.
  • Devansh Jalota, Kiril Solovey, Matthew Tsao, Stephen Zoepf, and Marco Pavone. “Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment.” Autonomous Agents and Multi-Agent Systems, 37(2), Article 32, 2023.
  • Devansh Jalota, Marco Pavone, Qi Qi, and Yinyu Ye. “Fisher Markets with Linear Constraints: Equilibrium Properties and Efficient Distributed Algorithms.” Games and Economic Behavior, 141: 223–260, 2023.
  • Devansh Jalota, Dario Paccagnan, Maximilian Schiffer, and Marco Pavone. “Online Routing Over Parallel Networks: Deterministic Limits and Data-Driven Enhancements.” INFORMS Journal on Computing, 35(3): 560–577, 2023.
  • Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, and Marco Pavone. “Online Learning for Traffic Routing under Unknown Preferences.” Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 3210–3229, 2023.
  • Devansh Jalota, Jessica Lazarus, Alexandre M. Bayen, and Marco Pavone. “Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design.” Proceedings of the IEEE Conference on Decision and Control (CDC), 4124–4129, 2023.
  • Devansh Jalota, Haoyuan Sun, and Navid Azizan. “Online Learning for Equilibrium Pricing in Markets under Incomplete Information.” Proceedings of the IEEE Conference on Decision and Control (CDC), 4996–5001, 2023.