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About

Alfredo Garcia is Professor and Chair of the Department of Systems and Information Engineering at the University of Virginia. His research asks how systems of interacting decision-makers — people, organizations, and increasingly AI agents — learn, respond to incentives, and can be designed to work well together. He began by studying strategic behavior in engineered markets and networks, using game theory and dynamic optimization to design pricing and allocation mechanisms for electricity markets and telecommunications. That same focus now drives his work on AI: inferring what people want from how they act in order to align large language models with human feedback, and modeling human attention and decision-making so that people and automation can work together safely. His work draws on reinforcement learning, Bayesian inference, game theory, and distributed optimization, and has appeared in Operations Research, IEEE Transactions on Automatic Control, Human Factors, and NeurIPS and ICLR.

Learn more about his group’s work on the Garcia Research lab site, including research areas and a full list of publications.

Education

Ph.D., Industrial & Operations Engineering, University of Michigan

D.E.A Diplome d'Etudes Approfondies, Automatique et Informatique Industrielle; Universite de Toulouse III, Toulouse, France

Selected Publications

Retraction-Free Decentralized Non-Convex Optimization With Orthogonal Constraints Y. Sun, S. Chen, A. Garcia, and S. Shahrampour. IEEE Transactions on Automatic Control, 2026.
DOI
Adaptive Rank Control for Robust Reinforcement Learning Ch. Li, J. Leng, J. Li, Y. Sun, S. Chen, S. Shahrampour, and A. Garcia. Transactions on Machine Learning Research, 2026.
OpenReview
Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization Ch. Li, A. Elmahdy, A. Boyd, Z. Wang, S. Zeng, A. Garcia, P. Bhatia, T. Kass-Hout, X. Cao, and M. Hong. Conference on Language Modeling (COLM), 2026.
arXiv
Inferring Hidden Attentional States in Driving: A Bayesian Approach to Modeling Distraction and Secondary Task Engagement L. Kashyap, Z. Wang, Y. Chang, M. Zahabi, and A. Garcia. Human Factors, 2026.
DOI
Regularized Q-Learning with Linear Function Approximation J. Xi, A. Garcia, and P. Momcilovic. IEEE Transactions on Automatic Control, 2025.
DOI
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees S. Zeng, M. Hong, and A. Garcia. Operations Research, 2024.
DOI

Courses Taught

SIE 3060 Stochastic Decision Models