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. His research primarily focuses on advancing machine learning (ML) and data mining in areas such as robustness and security of AI through anomaly detection and out-of-distribution (OOD) detection, efficient and scalable AI with ML systems and automation, and applications in security, finance, and healthcare. He has contributed over 40 papers to leading venues. Dr. Zhao is recognized for his work in open-source ML systems, having created more than 10 projects with over 20,000 GitHub stars and 22 million downloads. His notable projects, including PyOD, PyGOD, TDC, and ADBench, are used in organizations like NASA and Morgan Stanley for high-stakes applications. He has received awards such as Amazon Research Awards, AAAI New Faculty Highlights Awards, Google Cloud Research Innovators, Norton Fellowship, Meta AI4AI Research Award, and CMU Presidential Fellowship. In his professional capacity, he serves as an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), an action editor of the Journal of Data-centric Machine Learning Research (DMLR), a workflow co-chair for KDD 2023, and an area chair for numerous ML conferences.
Research Summary
My work focuses on creating robust, efficient, and automated machine learning (ML) and data mining (DM) algorithms, systems, and applications. My primary areas of interest are:
1. Robustness and Security of AI: Enhancing the robustness and security of AI systems through out-of-distribution (OOD) detection, outlier detection, and anomaly detection.
2. Efficient and Scalable AI: Developing efficient and scalable ML systems and automation techniques.
3. Applications in Security, Finance, and Healthcare: Applying AI technologies to address complex problems in security, finance, and healthcare sectors.
4. Integration with foundation models and generative AI: How do these emerging directions benefit anomaly and OOD detection, and vice versa.
Research Keywords: (1) Robustness and Security of AI: OOD Detection, Outlier Detection, Anomaly Detection (2) Efficient and Scalable AI: ML Systems (MLSys), Automated ML, Decentralized Learning (3) Applications: AI for Security, Finance, Healthcare (4) Integration with Foundation Models and GenAI: LLMs for anomaly and OOD detection.
Awards
- 2024 Amazon Amazon Research Awards
- 2024 Google Google Cloud Research Innovators
