Yue Zhao
Avatar of Yue Zhao
Assistant Professor
Thomas Lord Department of Computer Science
School of Advanced Computing

University of Southern California

Los Angeles, CA, USA
Email:
Google Scholar | Short Bio | GitHub | Twitter | LinkedIn | 知乎 (微调) | 小红书 | 微信 | 250k+ followers in total

Collaboration with Me. I am open to external opportunities for invited talks, research collaborations, and employment (only on the part-time/advising/visiting basis). Let us have a chat by email. I frequently visit major cities, e.g., Seattle, NYC, Chicago, Boston, Atlanta, and Bay Area to meet people, give talks, and host social events.

Research Interests: My research focuses on building Robust, Trustworthy, and Scalable AI systems by addressing challenges at three distinct but connected levels: the Principle Level, the Knowledge & Generation Level, and the System Level. Through these levels, I integrate reliable detection methods, graph-based structured knowledge, generative modeling, and open-source tools to advance AI4Science, healthcare, finance, and political science.

  1. Robust and Trustworthy AI (Principle): Ensuring AI systems can detect outliers, anomalies, and out-of-distribution data to provide trust, fairness, and transparency across different domains.
    Keywords: OOD Detection, Outlier Detection, Anomaly Detection, Trustworthiness
  2. Structured and Generative AI for Science and Applications (Knowledge): Leveraging graph-based learning to understand interconnected data and applying generative AI methods, large language models, and foundation models to address scientific challenges in drug discovery, synthetic clinical trials, and political forecasting.
    Keywords: Graph Learning, Graph Anomaly Detection, LLMs, Foundation Models, AI4Science, Drug Discovery
  3. Scalable and Open-Source AI (System): Developing efficient tools and frameworks for automated model selection, hyperparameter optimization, and large-scale anomaly detection. As the creator of PyOD (25M+ downloads, used by NASA, Tesla, etc.), I lead 10+ open-source projects, including PyGOD, TDC, and ADBench, which collectively have earned more than 20,000 GitHub stars, accelerating AI adoption and impact.
    Keywords: Automated ML, Distributed Systems, Open-source AI, Scalability

Biography.

Lab Openings. We are peacefully welcoming new members to the FORTIS Lab!

Ph.D. Students (2 Ph.D. students for Fall 2026):
  • Due to the large number of interested candidates, future Ph.D. students are expected to have prior collaboration with me + a few published papers (not necessarily with me) in top-tier ML, System, CV, or NLP conferences/journals.
  • Application Deadline: by June 2025, as we need at least 6 month collaboration to know each other.
Research Interns (Any Time, All Year Round):
  • We welcome both undergraduate and graduate interns from USC and other institutions.
  • Preferred candidates are located in North America for time zone compatibility.
Application Process: To apply for either opportunities, complete the Application Form, email me after submitting the form, and review the FORTIS Lab website for more information before reaching out.

✈ News and Travel

[Dec 2024] We have a new paper on a major upgrade (PyOD 2) to our library for outlier detection with LLM-based model selection; see our Preprint!

[Dec 2024] We have a new paper evaluating how LLMs can help with anomaly detection (AD-LLM); see our Preprint!

[Dec 2024] We have a new paper on integrating LLMs into political science (Political-LLM); see our Preprint!

[Dec 2024] We have a new paper on Personalized Multimodal Large Language Models (MLLMs) providing a survey on personalization techniques, evaluation metrics, and benchmarks for these models, with Junda Wu, Hanjia Lyu, Yu Xia, Zhehao Zhang, Joe Barrow, Ishita Kumar, Mehnoosh Mirtahebi, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Namyong Park, Sungchul Kim, Huanrui Yang, Subrata Mitra, Zhengmian Hu, Nedim Lipka, Jiebo Luo, Julian McAuley. Preprint.

[Dec 2024] We have a new paper introducing a comprehensive benchmark for NLP anomaly detection (NLP-ADBench); see our Preprint!

[Dec 2024] We have a new paper on personalized hierarchical federated learning for IoT (H-FedSN); see our Preprint!

[Dec 2024] We have a new paper on automating AI-aided drug discovery programming through LLM multi-agent collaboration; see our Preprint!

[Nov 2024] We have a new paper on concept-based zero-shot OOD detection; see our Preprint!

[Nov 2024] We have a new paper on personal biometric defense against malicious generative editing; see our Preprint!

[Nov 2024] We have a new paper on dynamic prototype updating for multimodal out-of-distribution detection; see our Preprint!

[Nov 2024] We have a new paper on data augmentation for anomaly detection accepted to IEEE TNNLS; see our Preprint!

[Oct 2024] We have a new paper on using LLMs for the US election prediction; see our Preprint!

[Oct 2024] We have a new paper on label-efficient graph learning for OOD; see our Preprint!

[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!

🏅 Awards and Grants

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