Dr. Yue Zhao is an Assistant Professor of Computer Science at the University of Southern California, where he leads the FORTIS Lab. His lab builds methods, benchmarks, and open-source tools for AI risk audit and control across the deployment stack: anomaly and out-of-distribution detection in data, trust and robustness evaluation of foundation models, and runtime audit and control of deployed agent systems. This agenda builds on a decade of work on anomaly and outlier detection at scale.
Dr. Zhao has authored over 80 peer-reviewed papers in top-tier venues and is internationally recognized for his open-source contributions, including PyOD, ADBench, TrustLLM, agent-audit, and Aegis, which collectively exceed 42 million downloads and 24,000 GitHub stars. PyOD is the subject of five published books, is recommended as a primary tool for out-of-distribution detection by the U.S. Department of Defense CDAO Generative AI Responsible AI Toolkit, and is used by OpenAI, Apache Beam, Amazon, Walmart, Databricks, and the European Space Agency (for spacecraft anomaly detection). TrustLLM is cited in a U.S. Senate committee report, a NIST special publication on adversarial machine learning, the U.S. Department of Defense CDAO Generative AI Responsible AI Toolkit, and the International AI Safety Report 2026, and serves as an official benchmark in all three editions of the Future of Life Institute AI Safety Index.
He has received numerous honors, including the NVIDIA Academic Grant Program Award, the Foresight Institute AI for Safety & Science Nodes Grant, the Capital One Research Award, multiple Amazon Research Awards, AAAI New Faculty Highlights, Google Cloud Research Innovators, the Norton Labs Fellowship, the Meta AI4AI Research Award, the Carnegie Mellon University Presidential Fellowship, the 2025 SIGSPATIAL Best Short Paper Award, and the Second Prize CCC Award at the IEEE ICDM 2025 BlueSky Track.
He serves as an Associate or Action Editor for ACM Transactions on AI for Science, IEEE Transactions on Neural Networks and Learning Systems, and the Journal of Data-Centric Machine Learning Research, and as an Area Chair for major conferences including ICLR, ICML, and NeurIPS.