Tran Research Group

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Advancing data-driven autonomy through interdisciplinary research.

Welcome! Our goal is to understand how to better design autonomous systems able to adapt and perform in dynamic, uncertain, and adversarial environments, as well as cyber-physical systems able to operate in a resilient and sustainable manner. Our approach is to develop mathematical theories and models to understand underlying behaviors of these systems, and use that understanding to develop algorithms and methodologies for improving existing systems or designing new ones. Our work is primarily data-driven, as we strive to utilize modern computing capabilities and increasingly available data sources (e.g., sensors and public data) to develop novel algorithms, design methods, and models. We are highly interdisciplinary, incorporating ideas from disciplines such as aerospace engineering, computer science, design, social sciences, and statistical physics. Current methods we use include: machine learning, reinforcement learning, optimization, and graph theory. Current applications include: autonomous multi-agent systems, transportation systems, and complex engineered systems.

We are always looking for motivated graduate and undergraduate students to join our team! Interested students are encouraged to contact Huy by email with a brief description of their research interests and resume or CV.