Jingqi Li

About me:

I am an EECS PhD candidate at UC Berkeley, where I am very fortunate to be co-advised by Professor Claire Tomlin and Professor Somayeh Sojoudi. I am a student member of Berkeley AI Research (BAIR) Lab. Previously, I received my Master's degree in Electrical Engineering from the University of Pennsylvania in 2019, co-advised by Professor Victor M. Preciado and Professor George J. Pappas.  I received my Bachelor's degree in  Aerospace Engineering from Beihang University, China, in 2016. 

My research interest lies at the intersection of dynamic game theory, control, and deep RL. The primary goal of my research is to ensure safe,  strategic decision-making in real-world scenarios. I'm also exploring the application of my work to Embodied AI,  Human-Robot Interaction,  Autonomous Driving,  Advanced Air-Mobility, etc. 

Currently, I am focusing on safe game-theoretic control under information asymmetry:   

1. [Computationally efficient strategic decision-making] Robots perform well in structured environments but often struggle in unstructured settings—especially when others aren’t cooperative, e.g., autonomous driving. So, how can they learn/compute strategic decisions with provably convergence and safety guarantees? (e.g., [17], [16], [15], [9], [7], [5])

2. [Addressing and leveraging information asymmetry] A key challenge in real-world multi-agent problems is intent uncertainty. For example,  a chatbot needs to understand human intent, and an autonomous vehicle must grasp the intent of other drivers on the road. So, how can agents handle this information asymmetry and interact strategically to achieve their goals? (e.g., [8], [10], [12], [11])

3. [Game theory for safe, socially intelligent robots] If we want robots to become part of our lives, they must be as socially intelligent as humans. Dynamic game theory lets agents see things from others' perspectives, allowing robots to understand the intent and interact safely. I am working on combining control, dynamic games, and generative modeling to pave the way for designing safe, socially intelligent, multi-agent embodied AI systems. (e.g., [12], [17], [16], [14], [13], [8], [7], [3], [2])

Publications: Google Scholar, ResearchGate, 

[17] J. Li, S. Sojoudi, C. Tomlin, D. Fridovich-Keil, "The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games", SIAM Journal on Optimization (SIOPT), accepted, 2024.

[16] J. Li, D. Lee, J. Lee, K. Dong, S. Sojoudi, C. Tomlin, "Certifiable Deep Learning for Reachability Using a New Lipschitz Continuous Value Function", submitted to IEEE Robotics-Automation Letters (R-AL), 2024.  Video presentation.

[15] X. Liu, J. Li, F. Fotiadis, M. O Karabag, J. Milzman, D. Fridovich-Keil, U. Topcu, "Policies with Sparse Inter-Agent Dependencies in Dynamic Games: A Dynamic Programming Approach", submitted, 2024.

[14] G. Chenevert, J. Li, S. Bae, D. Lee, "Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients", submitted to IEEE American control conference, 2025.

[13] C. Chiu*, J. Li*, M. Bhatt, N. Mehr, "To what extent do open-loop and feedback Nash equilibria diverge in general-sum linear quadratic dynamic games?", IEEE Control Systems Letters (L-CSS), accepted, 2024.

[12] J. Li, A. Siththaranjan, S. Sojoudi, C. Tomlin, A. Bajcsy, "Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations", submitted to IEEE Transactions on Control Systems Technology (TCST), 2024.

[11] C. Strong, K. Stocking, J. Li, T. Zhang, J. Gallant, C. Tomlin, "A framework for evaluating human driver models using neuroimaging", L4DC, 2024. 

[10] D. Papadimitriou, J. Li, "Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization", IEEE CDC, 2023.

[9] J. Li, C. Chiu, L. Peters, F. Palafox, M. Karabag, J. Alonso-Mora, S. Sojoudi, C. Tomlin, D. Fridovich-Keil, "Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games", IEEE CDC, 2023.

[8] J. Li, C. Chiu, L. Peters, S. Sojoudi, C. Tomlin, D. Fridovich-Keil, "Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories", AAMAS 2023.

[7] J. Li, D. Fridovich-Keil, S. Sojoudi, C. Tomlin, "Augmented Lagrangian Method for Instantaneously Constrained Reinforcement Learning Problems", in Proceedings of the 60th IEEE Conference on Decision and Control, 2021.

[6] B. Anderson, Z. Ma, J. Li, and S. Sojoudi, "Partition-based Convex Relaxations for Certifying the Robustness of ReLU Neural Networks", submitted to Journal of Machine Learning Research (JMLR), 2020.

[5] B. Anderson, Z. Ma, J. Li and S. Sojoudi, "Tightened convex relaxations for neural network robustness certification", in Proceedings of the 59th IEEE Conference on Decision and Control, 2020.

[4] J. Li, X. Chen, S. Pequito, G. J. Pappas, and V. M. Preciado, "On the structural target controllability of undirected networks",  IEEE Transactions on Automatic Control, 2020.

[3] J. Li, X. Chen, S. Pequito, G. J. Pappas, and V. M. Preciado, "Resilient structural stabilizability of undirected networks", in Proceedings of IEEE American Control Conference, 2019.

[2] J. Li, X. Chen, S. Pequito, G. J. Pappas, and V. M. Preciado, "Structural target controllability of undirected networks", in Proceedings of the 57th IEEE Conference on Decision and Control, 2018, Invited paper.

[1] L. Feng, J. Li, and J. Xiao, "Temperature effects on excited state of strong-coupling polaron in an asymmetric RbCl quantum dot", Modern Physics Letters B, Vol. 29, No. 02, 1450261, 2015.

Thesis and other works:

• J. Li, "A structural approach to the control, design, and analysis of networked dynamical systems", 2019, University of Pennsylvania, MSE in Electrical and Systems Engineering.