Assistant Professor of Computer Science
Education
- 2023, Doctoral Degree, Information Systems, Carnegie-Mellon University
- 2016, Master's Degree, Computer Science, University of Toronto
- 2015, Bachelor's Degree, Computer Engineering, University of Cincinnati
Biography
Dr. Yue Zhao is an Assistant Professor of Computer Science at the University of Southern California and a faculty member of the USC Machine Learning Center. He leads the FORTIS Lab (Foundations Of Robust Trustworthy Intelligent Systems), where his research focuses on building reliable, trustworthy, and efficient AI systems. His work spans robust foundations (e.g., anomaly and out-of-distribution detection), graph learning, large language and generative models for societal applications (e.g., political forecasting, AI for science), and scalable open-source ML systems. Dr. Zhao has authored over 60 papers in top-tier venues and is internationally recognized for his open-source contributions—including PyOD, PyGOD, TDC, and TrustLLM—which collectively exceed 30 million downloads and 20,000 GitHub stars, ranking him among the top 800 developers worldwide. His tools are used by NASA, Tesla, Morgan Stanley, and the U.S. Senate. He is a recipient of multiple honors, including the Capital One Research Award, Amazon Research Awards, AAAI New Faculty Highlights, Google Cloud Research Innovators, Norton Fellowship, Meta AI4AI Research Award, and the CMU Presidential Fellowship. He serves as associate or action editor for ACM Transactions on AI for Science, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), and Journal of Data-centric Machine Learning Research (DMLR), and regularly acts as area chair for ICLR, ICML, AAAI, and AISTATS.
Research Summary
1. Robust & Trustworthy AI: Detecting the Unexpected.
I design core algorithms to detect anomalies, out-of-distribution (OOD) data, and outliers across diverse modalities (including graph-structured data). These methods reinforce AI systems against rare or unseen scenarios, enhancing reliability, security, and interpretability.
Keywords: Anomaly Detection, OOD Detection, Trustworthy AI, Graph Anomaly Detection
2. AI for Science & Society: Foundation Models in Action.
By pairing robust detection with large language models (LLMs) and generative AI (GenAI), I tackle interdisciplinary challenges—from scientific discovery to political forecasting and computational social science. This approach bridges algorithmic research with real-world decision-making and public policy.
Keywords: AI for Science, Generative AI, LLMs, Political Forecasting, Computational Social Science
3. Scalable, Automated & Open-source ML Systems.
To ensure widespread adoption, I build reproducible and efficient tools—most notably PyOD (27M+ downloads) for anomaly detection, along with PyGOD, ADBench, and other libraries with 20K+ GitHub stars (top 800 worldwide). My work emphasizes automated model selection, distributed inference, and user-friendly designs, democratizing advanced ML across academia and industry.
Keywords: ML Systems, Automated ML, Open-source AI, Distributed Computing
Awards
- 2024 Amazon Amazon Research Awards
- 2024 Google Google Cloud Research Innovators
- 2024 Capital One Research Awards
- 2024 Association for the Advancement of Artificial Intelligence AAAI New Faculty Highlights
