Jingqi Li
Hello! I'm Jingqi Li, a Peter O'Donnell Jr. Postdoctoral Fellow at the Oden Institute for Computational Engineering and Sciences, UT Austin. I work with Prof. David Fridovich-Keil on game-theoretic control and learning. I received my Ph.D. in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2025, where I was advised by Prof. Claire Tomlin and Prof. Somayeh Sojoudi.
Research interests:
As we deploy more autonomous systems, agents must make decisions while accounting for the actions of others. This raises a fundamental question: how can agents reason, learn, and act strategically with limited and asymmetric information?
My research addresses this question through the lenses of game theory, control, optimization and reinforcement learning. Broadly, I study multi-agent decision-making under uncertainty, focusing on differentiable dynamic games (whose equilibria are differentiable with respect to game parameters), information structure, learning, and safety, as well as the computational methods that enable these systems to scale in practice. My work is motivated by applications in autonomous driving, drone racing, manipulation, advanced air mobility, and infrastructure-scale autonomy.
Current directions:
Differentiable Dynamic Games for Strategic Decision-Making: Optimization and learning-based approaches for strategic decision-making, leveraging differentiable structure and principled approximations for efficient computation and analysis of equilibria;
Inference and Learning under Information Asymmetry: In practice, agents' uneven access to knowledge about their environment—information asymmetry—can disrupt coordination and lead to failures. Beyond seeking to align such asymmetry, I study how structured information asymmetry can instead shape strategic interaction and be harnessed to steer coordination outcomes;
Decentralized Strategic Autonomy: Developing theoretical and algorithmic foundations for decentralized strategic autonomy, enabling agents to make decisions, learn, and coordinate under limited information, with hardware robotics validation.
Selected works:
[SIOPT '24] We developed a differentiable approximation of the optimality conditions for constrained nonlinear feedback Stackelberg games with continuous action spaces. By approximating feedback game equilibrium conditions as differentiable equations subject to feedback information constraints, we propose a provably convergent inexact Newton method to solve constrained feedback nonlinear games.
[RA-L '25] Leveraging Lipschitz continuity, we developed a reachability learning framework offering deterministic safety assurance under bounded uncertainties, extending beyond the probabilistic safety guarantees of prior works. Our method was verified in drone racing hardware experiments. [video]
[AAMAS '23] Using the differentiable equilibria structure, we introduced an efficient likelihood-gradient approximator for inverse feedback nonlinear games, facilitating intent inference under information asymmetry.
I'm on the faculty job market this cycle (2026–27) [CV]