Mauricio Tec is a postdoctoral researcher specializing in representation learning for decision-making. His research focuses on developing representations that support counterfactual reasoning, generalize across datasets and tasks, and integrate external commonsense knowledge. He integrates advancements from reinforcement learning, foundation models like self-supervised learning and large language models (LLMs), and statistical techniques from Bayesian and causal inference. Mauricio has also worked on representation learning for structured domains such as images, graphs, spatiotemporal data, and higher-order topological data. His work has been published in top-tier conferences such as NeurIPS, ICLR, and AAAI, as well as in scientific journals like the Proceedings of the National Academy of Sciences. His research is supported by the National Science Foundation and the National Institutes of Health and has significant implications for public health, particularly in designing AI-driven interventions and policies for climate change adaptation and mitigation.
Dr. Tec earned his Ph.D. in Statistics from the University of Texas at Austin, focusing on reinforcement learning, computer vision, and spatial data modeling. He held internships at Meta AI (FAIR) and Intel AI and was a member of the UT Austin Villa Robot Soccer Team. Prior to his Ph.D., Mauricio completed a B.S. in Applied Mathematics at ITAM and an M.S. in Pure Mathematics at the University of Cambridge.