Manxi Wu 

About me

I joined Cornell University Operations Research and Information Engineering as an assistant professor in 2022. Previously, I was a research fellow in the Learning and Games program at the Simons Institute for the Theory of Computing, and a postdoctoral researcher at EECS, Berkeley. 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 tools in game theory, information and incentive design for improving the sustainability and resilience of urban networks. My research focuses on the following two theme: 

1. Information design and learning in games. I study the design of information mechanisms for navigation and mobility platforms. I study centralized and decentralized learning dynamics in games.

2. Sustainable and equitable mobility. I design incentive mechanisms for improving the efficiency and equity in transportation systems. I develop scalable algorithms for optimizing electric vehicle deployment and infrastructure expansion.

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.

Journals under review and working papers:

[W1.] Ozan Candogan and Manxi Wu. Information design for spatial resource allocation. Conference version accepted in the 19th Conference on Web and InterNet Economics (WINE), 2023.

[W2.] 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. 

[W3.]  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 in Transportation Science, 2023.

[W4.] Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, and Manxi Wu. Market design for dynamic pricing and pooling in capacitated networks. Under review in Operations Research, 2023.

[W5.] Chinmay Maheshwari, Manxi Wu, Druv Pai, and Shankar Sastry. Independent and decentralized learning in Markov potential games. R & R in IEEE Transactions and Automatic Control, 2022.

[W6.] Sander Aarts, Manxi Wu, and David Shmoys. Sharing the Cost of IoT Wireless Coverage with a Strengthened Linear Programming Formulation, 2023.

[W7.] Zhanhao Zhang, Ruifan Yang, and Manxi Wu. Capacity allocation and pricing of high occupancy toll lane systems with heterogeneous travelers. Conference version accepted in the 62nd IEEE Conference on Decision and Control (CDC), 2023.

Ongoing works (draft available soon):

[O1.] Jim Dai, Manxi Wu, and Zhanhao Zhang. Scalable Deep Reinforcement Learning for Ride-Hailing with Electric Vehicles, 2023.

Peer Reviewed Conference Proceedings:

[C1.] Ozan Candogan and Manxi Wu. Information design for spatial resource allocation. Accepted in the 19th Conference on Web and InterNet Economics (WINE), 2023.

[C2.] 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. 

[C3.] Sreenivas Gollapudi, Kostas Kollias, Chinmay Maheshwari, and Manxi Wu. Online learning for traffic navigation in congested networks. In International Conference on Algorithmic Learning Theory, pages 642–662. PMLR, 2023.

[C4.] 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.

[C5.] 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.

[C6.] 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.

[C8.] 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.

[C9.] Manxi Wu, Saurabh Amin, and Asuman Ozdaglar. Bayesian learning with adaptive load allocation strategies. In Learning for Dynamics and Control, pages 561–570. PMLR, 2020.

[C10.] Manxi Wu and Saurabh Amin. Learning an unknown network state in routing games. IFAC-PapersOnLine, 52(20):345–350, 2019.

[C11.] 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.

[C12.] 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.

[C13.] 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:

Haripriya Pulyassary (Cornell ORIE, PhD student)

Zhanhao Zhang (Cornell ORIE, PhD student) co-advised with Jim Dai

Ruifan Yang (Cornell ORIE, PhD student)

Teaching:

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

Cornell University, School of Operations Research and Information Engineering

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

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