Profile picture of Thanh Nguyen

Thanh Nguyen

Assistant Professor
Computer Science
Phone: 541-346-3974
Office: 303 Deschutes Hall
Office Hours: CS471/571: 1:30 pm - 2:30 pm on Wednesdays and Fridays; CS410/510: 2:30 pm - 3:30 pm on Wednesdays and Fridays.
Research Interests: Artificial Intelligence, Machine Learning, Multi-agent Systems, Game Theory, Optimization


Thanh Nguyen is an Assistant Professor in the Department of Computer & Information Science at the University of Oregon. She completed her Postdoc in the Department of Computer Science & Engineering at the University of Michigan in Summer 2018. She received her Ph.D. from the Department of Computer Science at the University of Southern California (USC) in Summer 2016. While at USC, she was part of the USC Center for Artificial Intelligence in Society. Her work in the area of Artificial Intelligence is motivated by real-world societal problems, particularly in the areas of Sustainability, Public Safety and Security, Cybersecurity, and Public Health. Her recent awards include the Deployed Application Award (IAAI 2016) and Runner-up of the Best Innovative Application Paper Award (AAMAS 2016). Thanh has published extensively in several leading conferences in Artificial Intelligence, including IJCAI, AAAI, AAMAS, and GameSec. She has contributed to build the real-world wildlife-protection application PAWS (Protection Assistant for Wildlife Security), which has been extensively used by NGOs in conservation areas in multiple countries.


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

Research Interests

Thanh's work in the field of Artificial Intelligence (AI) is motivated by real-world societal problems, particularly in the areas of Public Safety and Security (e.g., urban crime prevention and counterterrorism), Cybersecurity (e.g., the protection of network data from stealthy botnets), Sustainability (e.g., wildlife and fish protection), and Public Health (e.g., the prevention of vaccination misinformation spread on social networks). The solutions she has developed employ techniques drawn not only from AI's various subfields, including Multi-Agent Systems, Game Theory, Machine Learning, and Optimization, but also from fields outside of AI, such as Cognitive Modeling and Conservation Biology.