Manxi Wu 

About me

I am an assistant professor of the Civil and Environmental Engineering department at University of California, Berkeley. Previously, I was assistant professor at Cornell University Operations Research and Information Engineering (2022- 2024), visiting research scientist at Google Research, and postdoctoral researcher at EECS, Berkeley (2021 - 2022). I completed my PhD in 2021 from the Institute for Data, Systems, and Society at MIT. I hold a M.S. in Transportation from MIT, and a B.S. in Applied Mathematics from Peking University. 

My research develops methods in game theory, multi-agent learning and optimization with applications in urban systems. My research has three main themes: 

1. Information and market design. I study the design of information mechanisms for navigation [J1] and ride-hailing platforms [W8]. I design market and incentive mechanisms for efficient resource sharing in networks [W6, W3]. 

2. Learning in static and Markov games. I study coupled belief-strategy learning dynamics in static games [J2]. I study decentralized multi-agent reinforcement learning algorithms in Markov games [W5, W4]. 

3. Applications: sustainable and equitable mobility. I develop scalable algorithms for optimizing electric vehicle deployment and infrastructure expansion [W7, O1]. I conduct game theoretic analysis and data validation on tolling systems and high occupancy toll lane design in California bay area [W1, W2]. 

Published Journal Articles: 

[J1.] Manxi Wu, Saurabh Amin, and Asuman E Ozdaglar. Value of information in Bayesian routing games. Operations Research, 69(1):148–163, 2021.

[J2.]  Manxi Wu, Saurabh Amin, and Asuman Ozdaglar. Convergence and stability of coupled belief–strategy learning dynamics in continuous games. Mathematics of Operations Research, 2024.

[J3.] Manxi Wu and Saurabh Amin. Securing infrastructure facilities: When does proactive defense help? Dynamic Games and Applications, 9:984–1025, 2019.

[J4.] Druv Pai, Michael Psenka, Chih-Yuan Chiu, Manxi Wu, Edgar Dobriban, and Yi Ma. Pursuit of a discriminative representation for multiple subspaces via sequential games. Journal of the Franklin Institute, 360(6):4135–4171, 2023.

[J5.] Chinmay Maheshwari, Manxi Wu, Shankar Sastry. Decentralized Learning in General-sum Markov Games, IEEE Control Systems Letters, 2024.

[J6.] Haripriya Pulyassary, and Manxi Wu. "Digital Transformation in Transportation Systems: Strategic User Behavior and System Efficiency." Tutorials in Operations Research: Smarter Decisions for a Better World. INFORMS, 2024. 244-274.

[J7.] (alphabetical) Xin Guo, Xinyu Li, Chinmay Maheshwari, Shankar Sastry, and Manxi Wu. "Markov $\alpha$ potential games." Accepted in IEEE Transactions on Automatic Control, 2025. 

[J8.] Chinmay Maheshwari, Manxi Wu, Druv Pai, and Shankar Sastry. Independent and decentralized learning in Markov potential games. Accepted in IEEE Transactions on Automatic Control, 2024.

[J9.] Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, and Shankar Sastry. Adaptive Incentive Design with Learning Agents. Accepted in IEEE Transactions on Automatic Control, 2024

Journals under review and working papers:

[W6.] (alphabetical) Jim Dai, Manxi Wu, and Zhanhao Zhang. Atomic Proximal Policy Optimization for Electric

Robo-Taxi Dispatch and Charger Allocation. 

[W5.] (alphabetical) Ozan Candogan and Manxi Wu. Information design for spatial resource allocation. Major revision in Management Science. 

[W4.] Haripriya Pulyassary, Kostas Kollias, Aaron Schild, David Shmoys, and Manxi Wu. Network Flow Problems with Electric Vehicles. Journa major revision in Math Programming. Conference version accepted in the 25th Conference on Integer Programming and Combinatorial Optimization (IPCO) 2024. 

[W3.] (alphabetical) Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, and Manxi Wu. Market design for dynamic pricing and pooling in capacitated networks. Journal major revision in ACM Transactions on Economics and Computation. Conference accepted in the 20th Conference on Web and InterNet Economics (WINE), 2024.

[W2.]  Chinmay Maheshwari, Kulkarni Kshitij, Druv Pai, Jiarui Yang, Manxi Wu, and Shankar Sastry. Congestion Pricing for Efficiency and Equity: Theory and Applications to the San Francisco Bay Area, Under review, 2023.

[W1.] Zhanhao Zhang, Ruifan Yang, and Manxi Wu. Capacity allocation and pricing of high occupancy toll lane systems with heterogeneous travelers. Journal under review. Conference version accepted in the 62nd IEEE Conference on Decision and Control (CDC), 2023.

Peer Reviewed Conference Proceedings:

[C15.] Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, and Manxi Wu. Market design for dynamic pricing and pooling in capacitated networks. Conference accepted in the 20th Conference on Web and InterNet Economics (WINE), 2024.

[C14.] Sander Aarts, Jacob Dentes, Manxi Wu, and David Shmoys. Sharing the Cost of IoT Wireless Coverage with a Strengthened Linear Programming Formulation, Workshop on Approximation and Online Algorithms (WAOA), 2024.

[C13.] (alphabetical) Ozan Candogan and Manxi Wu. Information design for spatial resource allocation. 19th Conference on Web and InterNet Economics (WINE), 2023.

[C12.] Haripriya Pulyassary, Kostas Kollias, Aaron Schild, David Shmoys, and Manxi Wu. Network Flow Problems with Electric Vehicles. Conference version accepted in the 25th Conference on Integer Programming and Combinatorial Optimization (IPCO) 2024. 

[C11.] (alphabetical) Sreenivas Gollapudi, Kostas Kollias, Chinmay Maheshwari, and Manxi Wu. Online learning for traffic navigation in congested networks. In International Conference on Algorithmic Learning Theory (ALT), pages 642–662. PMLR, 2023.

[C10.] Haripriya Pulyassary, Ruifan Yang, Zhanhao Zhang, and Manxi Wu. Capacity allocation and pricing of high occupancy toll lane systems with heterogeneous. Accepted in the 62nd IEEE Conference on Decision and Control (CDC), 2023.

[C9.] Aron Brenner, Manxi Wu, and Saurabh Amin. Interpretable machine learning models for modal split prediction in transportation systems. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 901–908. IEEE, 2022.

[C8.] Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, and S Shankar Sastry. Inducing social optimality in games via adaptive incentive design. In 2022 IEEE 61st Conference on Decision and Control (CDC), pages 2864–2869. IEEE, 2022.

[C7.] Chinmay Maheshwari, Kshitij Kulkarni, Manxi Wu, and S Shankar Sastry. Dynamic tolling for inducing socially optimal traffic loads. In 2022 American Control Conference (ACC), pages 4601–4607. IEEE, 2022.

[C6.] Manxi Wu, Devendra Shelar, Raja Gopalakrishnan, and Saurabh Amin. Optimal testing strategy for containing COVID-19: A case-study on Indian migrant worker population. In 2021 American Control Conference (ACC), pages 3145–3151. IEEE, 2021.

[C5.] Manxi Wu, Saurabh Amin, and Asuman Ozdaglar. Bayesian learning with adaptive load allocation strategies. In Learning for Dynamics and Control (L4DC), pages 561–570. PMLR, 2020.

[C4.] Manxi Wu and Saurabh Amin. Learning an unknown network state in routing games. IFAC-PapersOnLine, 52(20):345–350, 2019.

[C3.] Manxi Wu and Saurabh Amin. Information design for regulating traffic flows under uncertain network state. In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 671–678. IEEE, 2019.

[C2.] Manxi Wu, Li Jin, Saurabh Amin, and Patrick Jaillet. Signaling game-based misbehavior inspection in V2I-enabled highway operations. In 2018 IEEE Conference on Decision and Control (CDC), pages 2728–2734. IEEE, 2018.

[C1.] Manxi Wu, Jeffrey Liu, and Saurabh Amin. Informational aspects in a class of bayesian congestion games. In 2017 American Control Conference (ACC), pages 3650–3657. IEEE, 2017.

Advisees:

Kaiqi Jiang (UC Berkeley Transportation, PhD student)

Wolin Jiang (UC Berkeley, Systems Engineering, MS student)

Haripriya Pulyassary (Cornell ORIE, PhD student)

Zhanhao Zhang (Cornell ORIE, PhD student) 

Ruifan Yang (Cornell ORIE, PhD student)

Teaching:

Systems Analysis in Transportation                                                                                                      Spring 2025

UC Berkeley, Civil and Environmental Engineering

ORIE 4350/5350. Introduction to Game Theory (Undergraduate)         Fall 2022, Fall 2023, Fall 2024

Cornell University, School of Operations Research and Information Engineering

ORIE 7191. Information and market design for societal scale systems (Ph.D.)            Spring 2023, Spring 2024

Cornell University, School of Operations Research and Information Engineering

EE 290. Design of Societal Scale Systems: Information, Learning, and Incentives (Ph.D.)       Spring 2022

University of California, Berkeley, EECS