Yue Zhao
Avatar of Yue Zhao
Assistant Professor
Thomas Lord Department of Computer Science

University of Southern California

Los Angeles, CA, USA
Email:
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Collaboration with Me. I am open to external opportunities for invited talks, research collaborations, and employment (on the part-time/advising/visiting basis). Let us have a chat via email. I frequently visit major cities, e.g., Seattle, NYC, Chicago, Boston, and Bay Area to meet people and give in-person talks.

Lab Openings. We are seeking to recruit 2 Ph.D. students for Fall 2025. Applicants are required to have at least one paper published in a top ML, System, or LLM conference. We also have openings for undergraduate and graduate interns, both from USC and other institutions. For all positions, please complete this Google Form: Application Form. Additionally, Ph.D. candidates are required to email me directly after submitting the form. For further details, please visit the FORTIS Lab website before reaching out.

Research Interests: My research is centered on the development of robust, efficient, and automated machine learning (ML) algorithms, systems, and applications. My key areas of focus include:

  1. Robust and Trustworthy AI: Enhancing AI systems with capabilities in out-of-distribution (OOD) detection, outlier detection (OD), and anomaly detection to improve reliability and trust.
  2. Efficient and Automated AI: Developing ML systems that operate with minimal human supervision, optimizing for performance and automation.
  3. AI for Applications and Science: Applying AI technologies to solve complex problems in fields such as drug discovery, security, finance, healthcare, and political science.
  4. Foundation Models and Generative AI for OD/OOD: Investigating the interplay between OD/OOD and advanced models like large language models (LLMs), enhancing both fields.

Research Keywords: (1) Robust and Trustworthy AI: Out-of-Distribution Detection, Outlier Detection, Anomaly Detection (2) Efficient and Automated AI: Machine Learning Systems, Automated Machine Learning, Decentralized Learning (3) AI Applications: AI in Security, Finance, Healthcare, and Science (4) Foundation Models and Generative AI for OD/OOD: Large Language Models for Anomaly and OOD Detection

Open-source ML . I created PyOD (used by NASA, Tesla, Morgan Stanley, and more) - the most popular library for anomaly detection in 2017. Also, I have led more than 10 ML open-source initiatives, receiving 20,000 GitHub stars (top 0.002%) and >22M downloads. Popular ones: PyOD, PyGOD, TDC, ADBench

Biography.

Copy/paste from here :)

✈ News and Travel

[Oct 2024] Received Capital One Research Awards with Prof. Jieyu Zhao responsible AI for Finance!-->

[Oct 2024] We have a new paper on AI x Protein (Spotlight at NeurIPS Workshop on AI for New Drug Modalities): see our Preprint!

[Oct 2024] We have a new paper on automated OOD model selection (the best paper at KDD Resource Efficient Learning Workshop): see our Preprint!

[Sep 2024] MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities, led by Hao Dong @ ETHZ, will appear at NeurIPS 2024 as a spotlight paper!

[Sep 2024] We have a new paper on AI + Digital Twin; see our Preprint!

[Sep 2024] We have a new paper on GKAN: Graph Kolmogorov-Arnold Networks; see our Preprint!

🏅 Awards and Grants

As Principal Investigator (August 2023 onwards)
Prior to Principal Investigator Role (Before August 2023)