Thanh H. Nguyen

Associate Professor · Department of Computer Science · University of Oregon

I am an Associate Professor in the Department of Computer Science at the University of Oregon. My research interests span Artificial Intelligence, Multi-Agent Systems, Reinforcement Learning, Generative AI, and Optimization. My work is driven by real-world interdisciplinary challenges and develops advanced AI/ML methods with applications across diverse domains, including Physics (e.g., multislice electron ptychography), Healthcare (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).

Email Google Scholar DBLP Curriculum Vitae

Academic Positions

2025 – present
Associate ProfessorDepartment of Computer Science, University of Oregon
2018 – 2025
Assistant ProfessorDepartment of Computer Science, University of Oregon

Education & Training

2016 – 2018
Postdoctoral ResearcherComputer Science & Engineering, University of Michigan — with Prof. Michael P. Wellman and Prof. Satinder Singh
2011 – 2016
Ph.D. in Computer ScienceUniversity of Southern California — Adviser: Prof. Milind Tambe (TEAMCORE)
2005 – 2010
B.Sc.Center for Training of Excellent Students, Hanoi University of Science and Technology

Honors, Awards & Grants

2025
Intel Corporation Grant (Co-PI) Visualizing Defects and Dopants in Three Dimensions using Ptychography and Physics-Informed Machine Learning (2025–2026)
2025
CBDS UO–OHSU Collaborative Project Award (Co-PI) Knowledge Graph-Regularized Reinforcement Learning for Tumor Microenvironment Spatial Omics (2025–2027)
2020
Army Research Office Grant (Lead PI) Adversarial Reasoning: Tackling Sequential and Coordinated Attacks in Security Domains with Real-time Information (Grant W911NF-20-1-0344, 2020–2024)
2016
Deployed Application Award, IAAI For “Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security,” Innovative Applications of Artificial Intelligence
2016
Best Innovative Application Paper Award, Runner-up, AAMAS For “CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection,” 15th International Conference on Autonomous Agents and Multiagent Systems
2015
WiSE Merit Fellowship, University of Southern California Awarded to two women Ph.D. candidates in the Viterbi School of Engineering each year for exceptional work in their field

News

  • 2026Two papers accepted at ACL 2026 (main conference and Findings) on large language model distillation and representation learning.
  • 2026“CTPD: Cross Tokenizer Preference Distillation” accepted at AAAI 2026.
  • 2025“MisoDICE: Multi-Agent Imitation from Mixed-Quality Demonstrations” accepted at NeurIPS 2025.
  • 2025Promoted to Associate Professor in the Department of Computer Science at the University of Oregon.
  • 2025New research funding: an Intel Corporation grant on ptychography with physics-informed machine learning (Co-PI) and a CBDS UO–OHSU Collaborative Project Award on tumor microenvironment spatial omics (Co-PI).
  • 2025Serving as Area Chair for IJCAI 2025 and Co-Area Chair for AAMAS 2025 (Innovative Applications track).
  • 2025“O-MAPL: Offline Multi-Agent Preference Learning” accepted at ICML 2025 and “ComaDICE” accepted at ICLR 2025.

COMPASS Lab

The COMPASS Lab at the University of Oregon is an Artificial Intelligence (AI) research group dedicated to advancing the foundations of Learning and Decision Intelligence for interdisciplinary research. Our research in 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). We aim to bridge the gap between AI theory and practice by developing practical, computational solutions to these complex problems. Our 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.

Artificial IntelligenceMulti-Agent SystemsReinforcement Learning Generative AIGame TheoryOptimization

Research at a Glance

Methods Application Domains Multi-Agent Systems Reinforcement Learning Generative AI Game Theory Optimization ⚛ Physics multislice electron ptychography ⚕ Healthcare diabetes prevention · tumor analysis 🛡 Public Safety & Security crime prevention · counterterrorism 🌿 Sustainability wildlife & fish protection

Current Research Projects

AI/MLReinforcement Learning with Human Feedback

We develop new reinforcement learning (RL) algorithms guided by human feedback, addressing three key questions: (i) how human feedback can be effectively leveraged to enhance RL across learning paradigms, including offline RL, imitation learning, and multi-agent RL; (ii) how to handle heterogeneous human feedback data that vary in quality and reliability; and (iii) how large language models (LLMs) can be utilized to generate additional feedback data, helping to overcome the limitations of scarce human evaluations.

Agent Environment action state · reward 👤 human preferences LLM 🤖 synthetic feedback

Physics-AIPhysics-Informed AI for Ptychography

Electron microscopy combined with ptychography—a computational imaging technique—has enabled record-breaking resolution in electron microscopy. Despite recent advances, conventional reconstruction methods remain computationally intensive, often requiring hours or even days to converge. We are developing a novel predictive electron ptychography framework that synergistically integrates generative AI models with the forward model of conventional electron ptychography, producing high-resolution phase images of both thin and thick specimens in a single, real-time forward pass.

electron beam · specimen GenAI physics prior 3D phase image single forward pass

Healthcare-AITumor Microenvironment Modeling & Analysis

We apply advanced AI/ML methods to model and analyze the tumor microenvironment in collaboration with biomedical researchers.

tumor cells immune cells
Past Research Projects

Wildlife Protection in the Field: PAWS & CAPTURE

I contributed to developing PAWS (Protection Assistant for Wildlife Security), a deployed game-theoretic application for anti-poaching patrol planning. I led the wildlife-protection project in Indonesia in 2015 and participated in extending the application to protect tigers in Malaysia in 2016, collaborating with NGOs including the World Wildlife Fund, Panthera, Rimba, and the Wildlife Conservation Society. This work led to new research extending PAWS to Uganda, and PAWS has been extensively tested and deployed in both Malaysia and Uganda. The companion CAPTURE tool provides predictive anti-poaching analytics and was runner-up for the Best Innovative Application Paper Award at AAMAS 2016.

AI for Public Health

Designed to raise health-risk awareness in under-represented communities by (i) building new behavioral models of human health-risk behavior and applying machine learning techniques to learn the models, and (ii) developing new reinforcement learning algorithms to generate effective intervention plans that help improve people's health.

Deception in Security Games

Real-world security domains are often characterized by partial information: uncertainty (particularly on the defender's part) about actions or underlying characteristics of the opposing agent. To the extent that the defender relies on data, the attacker may modify its behavior to mislead the defender and manipulate learning outcomes to its long-term benefit. This project investigates strategic deception on the part of an attacker with private information.

Security in Data-based Decision Making

Studies the security of machine learning in decision-focused multi-agent environments, where AI models that combine learning and planning face increased threats from attacks on the learning component via exploitation of vulnerabilities of machine learning algorithms.

Information Leakage and Exploration

Investigates strategic behavior of players in exploiting and revealing private information to influence the decisions of other players, analyzing NP-hardness and designing efficient game-theoretic algorithms for optimizing information-revealing strategies in various classes of games, including security games.

Game Theory for Cybersecurity

Develops practical game-theoretic solutions for complex, large-scale cybersecurity domains involving dynamic stochastic interactions between network administrators and cybercriminals, applying simulation-based methodologies—particularly empirical game-theoretic analysis—and parameterized heuristic solutions.

People

Ph.D. Students

Mathew Huerta-Enochian Mathew Huerta-Enochian Ph.D. student (Fall 2026)
Michael Dushkoff Michael Dushkoff Ph.D. student (Fall 2024, co-advised with Prof. Allen D. Malony)

Master's & Undergraduate Students

Jonah Tang Jonah Tang Master's student (Biology)
Said Efendiyev Said Efendiyev Master's student
Eleanor Moseley Eleanor Moseley Undergraduate student

Alumni

Aabishkar TimalsinaM.Sc. (2026)
Mohammad HasanisaznaghiM.Sc. (2026)
Sarah KinseyPh.D. (2025)
Sabrina ReisB.Sc. (2023)
Gabriel PeeryB.Sc. (2023)
Czander TanM.Sc. (2022)
Jack Wolf2021 – 2022
Tayyab Tahir2021 – 2022
AJ SpiveyM.Sc. (2021)
Alyssa HuqueB.Sc. (2021)

Selected Publications

C Conference J Journal W Workshop B Book Chapter M Magazine S Symposium

2026

C
MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation [PDF]
Pham Khanh Chi, Quoc Phong Dao, Thuat Nguyen, Linh Ngo Van, Trung Le, Thanh H. Nguyen
In ACL-26: Proc. 64th Annual Meeting of the Association for Computational Linguistics (ACL), July 2026.
C
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining [PDF]
Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh H. Nguyen, Trung Le
In ACL-26 Findings: Findings of the Association for Computational Linguistics (ACL), July 2026.
C
CTPD: Cross Tokenizer Preference Distillation [PDF]
Truong Nguyen, Phi Van Dat, Ngan Nguyen, Linh Ngo Van, Trung Le, Thanh H. Nguyen
In AAAI-26: Proc. 40th AAAI Conference on Artificial Intelligence (AAAI), January 2026.

2025

C
MisoDICE: Multi-Agent Imitation from Mixed-Quality Demonstrations [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In NeurIPS-25: Proc. 39th Conference on Neural Information Processing Systems (NeurIPS), December 2025.
C
O-MAPL: Offline Multi-Agent Preference Learning [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In ICML-25: Proc. 42nd International Conference on Machine Learning (ICML), July 2025.
C
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In ICLR-25: Proc. 13th International Conference on Learning Representations (ICLR), April 2025.

2024

C
Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In NeurIPS-24: Proc. 38th Conference on Neural Information Processing Systems (NeurIPS), December 2024.
C
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Games [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In NeurIPS-24: Proc. 38th Conference on Neural Information Processing Systems (NeurIPS), December 2024.
C
Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary [PDF]
Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh H. Nguyen, Ayushman Kumar Singh
In IJCAI-24: Proc. 33rd International Joint Conference on Artificial Intelligence (IJCAI), August 2024.
C
Regret-based Defense in Adversarial Reinforcement Learning [PDF]
Roman Belaire, Pradeep Varakantham, Thanh H. Nguyen, David Lo
In AAMAS-24: Proc. 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2024.

2023

C
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning [PDF]
Changyu Chen, Ramesha Karunasena, Thanh H. Nguyen, Arunesh Sinha, Pradeep Varakantham
In NeurIPS-23: Proc. 37th Conference on Neural Information Processing Systems (NeurIPS), December 2023.
C
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations [PDF]
Hangzhi Guo, Thanh H. Nguyen, Amulya Yadav
In KDD-23: Proc. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), August 2023.
C
Building a Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning [PDF]
Sarah Kinsey, Jack Wolf, Nalini Saligram, Varun Ramesan, Meeta Walavalkar, Nidhi Jaswal, Sandhya Ramalingam, Arunesh Sinha, Thanh H. Nguyen
In IJCAI-23: Proc. 32nd International Joint Conference on Artificial Intelligence (IJCAI), AI for Good Track, August 2023.
C
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games [PDF]
The Viet Bui, Tien Mai, Thanh H. Nguyen
In AAMAS-23: Proc. 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2023.
C
Beyond NaN: Resiliency of Optimization Layers in the Face of Instability [PDF]
Wai Tuck Wong, Sarah Kinsey, Ramesha Karunasena, Thanh H. Nguyen, Arunesh Sinha
In AAAI-23: Proc. 37th AAAI Conference on Artificial Intelligence (AAAI), February 2023.
C
Behavioral Learning in Security Games: Threat of Multi-Step Manipulative Attacks [PDF]
Thanh H. Nguyen, Arunesh Sinha
In AAAI-23: Proc. 37th AAAI Conference on Artificial Intelligence (AAAI), February 2023.

2022

J
A Complete Analysis on the Risk of Using Quantal Response: When Attacker Maliciously Changes Behavior under Uncertainty [PDF]
Thanh H. Nguyen, Amulya Yadav
In Games, 2022, 13(6), 81. DOI: 10.3390/g13060081.
C
Information Design for Multiple Independent and Self-Interested Defenders: Work Less, Pay Off More [PDF]
Chenghan Zhou, Andrew Spivey, Haifeng Xu, Thanh H. Nguyen
In UAI-22: Proc. 38th Conference on Uncertainty in Artificial Intelligence (UAI), August 2022.
C
Algorithmic Information Design in Multi-Player Games: Possibilities and Limits in Singleton Congestion [PDF]
Chenghan Zhou, Thanh H. Nguyen, Haifeng Xu
In EC-22: Proc. 23rd ACM Conference on Economics and Computation (EC), July 2022.
C
When Can the Defender Effectively Deceive Attackers in Security Games? [PDF]
Thanh H. Nguyen, Haifeng Xu
In AAAI-22: Proc. 36th AAAI Conference on Artificial Intelligence (AAAI), February 2022.
C
An Exploration of Poisoning Attacks on Data-based Decision Making [PDF]
Sarah Kinsey, Wong Wei Tuck, Arunesh Sinha, Thanh H. Nguyen
In GameSec-22: Proc. 13th Conference on Decision and Game Theory for Security (GameSec), October 2022.
C
The Risk of Attacker Behavioral Learning: Can Attacker Fool Defender under Uncertainty? [PDF]
Thanh H. Nguyen, Amulya Yadav
In GameSec-22: Proc. 13th Conference on Decision and Game Theory for Security (GameSec), October 2022.
W
Poisoning Attacks on Data-based Decision Making: A Preliminary Study
Ryan Butler, Wong Wai Tuck, Arunesh Sinha, Thanh H. Nguyen
In AASG-22: 3rd Workshop on Autonomous Agents for Social Good at AAMAS, May 2022. (See the GameSec-22 full version.)
W
Algorithmic Information Design for Singleton Congestion Games [PDF]
Chenghan Zhou, Thanh H. Nguyen, Haifeng Xu
In GCLR-22: 2nd Workshop on Graphs and More Complex Structures for Learning and Reasoning at AAAI, February 2022.

2021

C
Countering Attacker Data Manipulation in Security Games [PDF]
Ryan Butler, Arunesh Sinha, Thanh H. Nguyen
In GameSec-21: Proc. 12th Conference on Decision and Game Theory for Security (GameSec), October 2021.
W
CounterNet: End-to-End Training of Counterfactual Aware Predictions [PDF]
Hangzhi Guo, Thanh H. Nguyen, Amulya Yadav
In Recourse-21: ICML Workshop on Algorithmic Recourse, July 2021. Runner-up, Best Paper Award.
W
Sequential Manipulative Attacks in Security Games [PDF]
Thanh H. Nguyen, Arunesh Sinha
In AASG-21: Workshop on Autonomous Agents for Social Good at AAMAS, May 2021.

2020

B
Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games [Link]
Haifeng Xu, Thanh H. Nguyen
In Adversary-Aware Learning Techniques and Trends in Cybersecurity. Springer, 2020.
C
Using One-Sided Partially Observable Stochastic Games for Solving Zero-Sum Security Games with Sequential Attacks [PDF]
Petr Tomášek, Branislav Bosansky, Thanh H. Nguyen
In GameSec-20: Proc. 11th Conference on Decision and Game Theory for Security (GameSec), November 2020.
C
Partial Adversarial Behavior Deception in Security Games [PDF]
Thanh H. Nguyen, Arunesh Sinha, He He
In IJCAI-20: Proc. 29th International Joint Conference on Artificial Intelligence (IJCAI), July 2020 (acceptance rate: 12.6%).
C
Decoding the Imitation Security Game: Handling Attacker Imitative Behavior Deception [PDF]
Thanh H. Nguyen, Nam Vu, Amulya Yadav, Uy Nguyen
In ECAI-20: Proc. 24th European Conference on Artificial Intelligence (ECAI), September 2020.
C
Tackling Imitative Attacker Deception in Repeated Bayesian Stackelberg Security Games [PDF]
Thanh H. Nguyen, Andrew Butler, Haifeng Xu
In ECAI-20: Proc. 24th European Conference on Artificial Intelligence (ECAI), September 2020.
C
Application-Layer DDoS Defense with Reinforcement Learning [PDF]
Yebo Feng, Jun Li, Thanh H. Nguyen
In IWQoS-20: Proc. IEEE/ACM 28th International Symposium on Quality of Service (IWQoS), June 2020.
W
Intelligent Tutoring Strategies for Students with Autism Spectrum Disorder: A Reinforcement Learning Approach [PDF]
Stephanie Milani, Amulya Yadav, Fei Fang, Thanh H. Nguyen, Zhou Fan, Saurabh Gulati
In AI4EDU-20: Workshop on Artificial Intelligence for Education at AAAI, February 2020.

2019

B
Adaptive Cyber Defenses for Botnet Detection and Mitigation [Link]
Massimiliano Albanese, Sushil Jajodia, Sridhar Venkatesan, George Cybenko, Thanh H. Nguyen
In Adversarial and Uncertain Reasoning for Adaptive Cyber Defense. Springer, 2019.
B
Empirical Game-Theoretic Methods for Adaptive Cyber-Defense [Link]
Michael P. Wellman, Thanh H. Nguyen, Mason Wright
In Adversarial and Uncertain Reasoning for Adaptive Cyber Defense. Springer, 2019.
C
Tackling Sequential Attacks in Security Games [PDF]
Thanh H. Nguyen, Amulya Yadav, Branislav Bosansky, Yu Liang
In GameSec-19: Proc. 10th Conference on Decision and Game Theory for Security (GameSec), November 2019.
C
Learning to Signal in the Goldilocks Zone: Improving Adversary Compliance in Security Games [PDF]
Sarah Cooney, Kai Wang, Elizabeth Bondi, Thanh H. Nguyen, Phebe Vayanos, Hailey Winetrobe, Edward A. Cranford, Cleotilde Gonzalez, Christian Lebiere, Milind Tambe
In ECML PKDD-19: Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2019 (acceptance rate: 17.7%).
C
Imitative Attacker Deception in Stackelberg Security Games [PDF]
Thanh H. Nguyen, Haifeng Xu
In IJCAI-19: Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019 (acceptance rate: 17.9%).
C
Warning Time: Optimizing Strategic Signaling for Security Against Boundedly Rational Adversaries (Extended Abstract) [PDF]
Sarah Cooney, Phebe Vayanos, Thanh H. Nguyen, Cleotilde Gonzalez, Christian Lebiere, Edward A. Cranford, Milind Tambe
In AAMAS-19: Proc. 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2019.
C
Deception in Finitely Repeated Security Games [PDF]
Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman
In AAAI-19: Proc. 33rd AAAI Conference on Artificial Intelligence (AAAI), January 2019 (acceptance rate: 16.2%).

2018

J
Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis [PDF]
Thanh H. Nguyen, Mason Wright, Michael P. Wellman, Satinder Singh
In Security and Communication Networks, 2018.
W
Deceitful Attacks in Security Games [PDF]
Thanh H. Nguyen, Michael P. Wellman, Arunesh Sinha
In AICS-18: Workshop on Artificial Intelligence for Cyber Security at AAAI, February 2018.

2017

M
PAWS — A Deployed Game-Theoretic Application to Combat Poaching [PDF]
Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Brian C. Schwedock, Milind Tambe, Andrew Lemieux
In AI Magazine, 2017, 38(1):23–36. DOI: 10.1609/aimag.v38i1.2710.
M
Keeping it Real: Using Real-World Problems to Teach AI to Diverse Audiences [PDF]
Nicole Sintov, Debarun Kar, Thanh H. Nguyen, Fei Fang, Kevin Hoffman, Arnaud Lyet, Milind Tambe
In AI Magazine, 2017, 38(2):35–47. DOI: 10.1609/aimag.v38i2.2733.
M
Predicting Poaching for Wildlife Protection [PDF]
Fei Fang, Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow, Colin Beale
In IBM Journal of Research and Development, 2017, 61(6):3:1–3:12. DOI: 10.1147/JRD.2017.2713584.
B
Trends and Applications in Stackelberg Security Games [PDF]
Debarun Kar, Thanh H. Nguyen, Fei Fang, Matthew Brown, Arunesh Sinha, Milind Tambe, Albert Xin Jiang
In Handbook of Dynamic Game Theory (edited by Tamar Basar and Georges Zaccour). Springer, 2017.
B
Methods for Addressing the Unpredictable Human Element in Security [Link]
(Joint Lead Author) Tracy Cui, Thanh H. Nguyen, James Pita, Richard S. John
In Improving Homeland Security Decisions, CREATE, 2017.
C
A Stackelberg Game Model for Botnet Traffic Exfiltration [PDF]
Thanh H. Nguyen, Michael P. Wellman, Satinder Singh
In GameSec-17: Proc. 8th Conference on Decision and Game Theory for Security (GameSec), October 2017.
W
Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis [PDF]
Thanh H. Nguyen, Mason Wright, Michael P. Wellman, Satinder Singh
In MTD-17: ACM Workshop on Moving Target Defense (MTD), October 2017.

2016

B
Towards a Science of Security Games [PDF]
Thanh H. Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe
In New Frontiers of Multidisciplinary Research in STEAM-H, 2016.
C
Combining Graph Contraction and Strategy Generation for Green Security Games [PDF]
Anjon Basak, Fei Fang, Thanh H. Nguyen, Christopher Kiekintveld
In GameSec-16: Proc. 7th Conference on Decision and Game Theory for Security (GameSec), November 2016.
C
Three Strategies to Success: Learning Adversary Models in Security Games [PDF]
Nika Haghtalab, Fei Fang, Thanh H. Nguyen, Arunesh Sinha, Ariel Procaccia, Milind Tambe
In IJCAI-16: Proc. 25th International Joint Conference on Artificial Intelligence (IJCAI), July 2016.
C
CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection [PDF]
Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow, Colin Beale
In AAMAS-16: Proc. 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2016. Runner-up, Best Innovative Application Paper Award.
C
Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security [PDF]
Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, Andrew Lemieux
In IAAI-16: Proc. 28th Innovative Applications of Artificial Intelligence Conference (IAAI), February 2016. Winner, Deployed Application Award.
S
From the Lab to the Classroom and Beyond: Extending a Game-Based Research Platform for Teaching AI to Diverse Audiences [PDF]
Nicole Sintov, Debarun Kar, Thanh H. Nguyen, Fei Fang, Kevin Hoffman, Arnaud Lyet, Milind Tambe
In EAAI-16: Proc. Symposium on Educational Advances in Artificial Intelligence (EAAI), February 2016.
W
Addressing Behavioral Uncertainty in Security Games: An Efficient Robust Strategic Solution for Defender Patrols [PDF]
Thanh H. Nguyen, Arunesh Sinha, Milind Tambe
In ParSocial-16: Proc. IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems, May 2016.

2015

J
From Physical Security to Cyber Security [PDF]
(Joint Lead Author) Arunesh Sinha, Thanh H. Nguyen, Debarun Kar, Matthew Brown, Milind Tambe, Albert Xin Jiang
In Journal of Cybersecurity, 2015, 1(1):19–35. DOI: 10.1093/cybsec/tyv007.
C
Making the Most of Our Regrets: Regret-based Solutions to Handle Payoff Uncertainty and Elicitation in Green Security Games [PDF]
Thanh H. Nguyen, Francesco M. Delle Fave, Debarun Kar, Aravind S. Lakshminarayanan, Amulya Yadav, Milind Tambe, Noa Agmon, Andrew J. Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba
In GameSec-15: Proc. 6th Conference on Decision and Game Theory for Security (GameSec), November 2015.
C
Beware the Soothsayer: Evaluating the Reliability of Attack Predictions in Stackelberg and Network Security Games [PDF]
Benjamin Ford, Thanh H. Nguyen, Nicole Sintov, Milind Tambe, Francesco Delle Fave
In GameSec-15: Proc. 6th Conference on Decision and Game Theory for Security (GameSec), November 2015.
S
Robust Resource Allocation in Security Games and Ensemble Modeling of Adversary Behavior [PDF]
Arjun Tambe, Thanh H. Nguyen
In ACM SAC-15: Proc. ACM Symposium on Applied Computing (ACM SAC) Track, April 2015.

2014

J
Game-Theoretic Target Selection in Contagion-based Domains [PDF]
Jason Tsai, Thanh H. Nguyen, Nicholas Weller, Milind Tambe
In The Computer Journal, 2014, 57(6):893–905. DOI: 10.1093/comjnl/bxt094.
C
Regret-based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty [PDF]
Thanh H. Nguyen, Amulya Yadav, Bo An, Milind Tambe, Craig Boutilier
In AAAI-14: Proc. 28th AAAI Conference on Artificial Intelligence (AAAI), July 2014.
C
Stop the Compartmentalization: Unified Robust Algorithms for Handling Uncertainties in Security Games [PDF]
Thanh H. Nguyen, Albert Xin Jiang, Milind Tambe
In AAMAS-14: Proc. 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2014.

2013

C
Monotonic Maximin: A Robust Stackelberg Solution Against Boundedly Rational Followers [PDF]
Albert Xin Jiang, Thanh H. Nguyen, Milind Tambe, Ariel D. Procaccia
In GameSec-13: Proc. 4th Conference on Decision and Game Theory for Security (GameSec), November 2013.
C
Analyzing the Effectiveness of Adversary Modeling in Security Games [PDF]
Thanh H. Nguyen, Rong Yang, Amos Azaria, Sarit Kraus, Milind Tambe
In AAAI-13: Proc. 27th AAAI Conference on Artificial Intelligence (AAAI), July 2013.
C
Modeling Human Adversary Decision Making in Security Games: An Initial Report (Extended Abstract) [PDF]
Thanh H. Nguyen, Amos Azaria, James Pita, Rajiv Maheswaran, Sarit Kraus, Milind Tambe
In AAMAS-13: Proc. 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.

2012

C
Security Games for Controlling Contagion [PDF]
Jason Tsai, Thanh H. Nguyen, Milind Tambe
In AAAI-12: Proc. 26th AAAI Conference on Artificial Intelligence (AAAI), July 2012.
S
Security Games on Social Networks [PDF]
Thanh H. Nguyen, Jason Tsai, Albert Jiang, Emma Bowring, Rajiv Maheswaran, Milind Tambe
In AAAI Fall Symposium, November 2012.

Teaching

Click to switch between day and night UNIVERSITY OF OREGON · EST. 1876 Deschutes Hall Department of Computer Science me Hi! me
Fall 2019-2025, Winter 2019-2021CS 471/571: Introduction to Artificial Intelligence
Fall 2020, Spring 2020, Fall 2024,
Winter 2022-2024, Winter 2026		CS 607: AI for Social Good
Winter 2025-2026, Spring 2026CS 315: Intermediate Algorithms
Winter 2022-2025CS 372M: Machine Learning for Data Science
Fall 2022-2023, Spring 2019-2022CS 410/510: Multi-Agent Systems

Contact

Email

tnguye11@uoregon.edu

Office

Room 303, Deschutes Hall
Department of Computer Science
University of Oregon
Eugene, OR 97403-1202

Profiles

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