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Computer Science
Portrait of Thanh Nguyen

Thanh Nguyen

Associate Professor
Computer Science
tnguye11@uoregon.edu
Phone: 541-346-3974
Office: 303 Deschutes Hall, 1202 University of Oregon, Eugene OR 97403-120
Research Interests: Artificial Intelligence, Multi-agent Systems, Reinforcement Learning, Generative AI, Game Theory, Optimization
Website: https://ix.cs.uoregon.edu/~thanhhng/

Biography

Thanh Nguyen is an Associate Professor in the Department of Computer Science at the University of Oregon (UO). Prior to joining UO, she was a postdoctoral researcher at the University of Michigan and earned her Ph.D. in Computer Science from the University of Southern California.

Her research in Artificial Intelligence (AI) is driven by interdisciplinary real-world challenges, with a particular focus on public safety and security, conservation, and public health. She develops advanced AI methods by integrating multi-agent systems, reinforcement learning, generative AI, and game theory to address complex decision-making problems under uncertainty while incorporating domain knowledge, operational constraints, and human preferences.

Her research has received multiple recognitions, including the IAAI-16 Deployed Application Award and the AAMAS-16 Runner-up Best Innovative Application Paper Award. She has published extensively in leading AI and machine learning venues, including NeurIPS, ICML, ICLR, AAAI, and IJCAI. Her AI technologies for wildlife conservation and public health have been evaluated and deployed in multiple countries around the world.

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Education

Ph.D., University of Southern California. 2016.

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Research Interests

Thanh's research in Artificial Intelligence (AI) is driven by real-world interdisciplinary challenges, particularly in Physics (e.g., multislice ptychography for 3D atomic reconstruction), Public Health (e.g., diabetes prevention and tumor microenvironment analysis), Public Safety and Security (e.g., urban crime prevention and counterterrorism), and Sustainability (e.g., wildlife and fish protection). She aims to bridge the gap between AI theory and practice by developing practical, computational solutions to these complex problems. Her work integrates methods from multiple areas of AI—including Multi-Agent Systems, Generative AI, Reinforcement Learning, and Optimization—as well as insights from disciplines beyond AI, such as Psychology, Physics, and Biology.

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