Yue Zhao
Avatar of Yue Zhao
Assistant Professor (profile picture generated by AI)
Thomas Lord Department of Computer Science

University of Southern California

Los Angeles, CA, USA
Email: yzhao010@usc.edu

Prospective Interns and Students. I am peacefully looking for unpaid interns (it is because USC does not allow remote paid interns) and prospective Ph.D. students (apply by Dec 15th for Fall 24 admission; full financial support). In short, you are expected to have a relevant paper to my research topics and strong programming skills for open-source ML and/or systems. See instructions (via email only; not WeChat) before reaching out.

Research Interests. I build fast and automated machine learning (ML) and data mining (DM) systems, with a focus on but not limited to anomaly detection and graph neural networks.

  1. Accelerate large-scale learning tasks by leveraging ML systems techniques.
  2. Automate unsupervised ML by model selection and hyperparameter optimization.
  3. Develop open-source ML tools to support applications in healthcare, finance, and security.

Research Keywords: Anomaly/Outlier/Out-of-Distribution (OOD) Detection, Unsupervised ML, ML Systems, Automated ML, AI for Science, Graph Neural Networks.

Open-source ML. I have led more than 10 ML open-source initiatives, receiving 16,000 GitHub stars (top 0.002%) and >20M downloads. Popular ones: PyOD, PyGOD, TDC, ADBench

Prior to USC. I got my Ph.D. in 4 years at CMU, working with Prof. Leman Akoglu, Prof. Zhihao Jia, and Prof. George H. Chen. I was a member of CMU Catalyst. I also collaborated with Prof. Jure Leskovec and Prof. Philip S. Yu.


✈ News and Travel

I have lost track on some of these nice things, obviously :(

[Sep 2023] Diffusion Models: A Comprehensive Survey of Methods and Applications is accepted to ACM Computing Surveys. Congrats to Lin Yang and other coauthors!

[Sep 2023] ADGym: Design Choices for Deep Anomaly Detection is accepted to NeurIPS 2023; I will be in New Orleans, again.


🍭 Recent Publications (2021-)

See my Google Scholar, DBLP, ORCID, and ResearchGate for the full publication list. Equal contribution; Corresponding author


Preprints & Working Papers

[w23g] Fast Unsupervised Deep Outlier Model Selection with Hypernetworks, with Xueying Ding, Leman Akoglu. Preprint.

[w23a] Weakly Supervised Anomaly Detection: A Survey, with Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu. Preprint.

[w22e] Hyperparameter Optimization for Unsupervised Outlier Detection, with Leman Akoglu. Preprint.


Conference Papers

Journal Papers


💯 Teaching

USC (as instructor):

  • Machine Learning (Spring 2024; to be finalized)

CMU (as teaching assistant):

  • Intro to Artificial Intelligence (Spring 2020-Spring 2022)
  • Digital Transformation (Spring 2022)
  • Statistics for IT Managers (Fall 2021)

University of Toronto (as teaching assistant):

  • Embedded Systems (Fall 2015)

🏅 Awards and Grants


- Meta AI4AI Research Award (co-PI), 2022

- Norton Labs Graduate Fellowship, 2022

- CMU Presidential Fellowship, 2019

- Mantei/Mae Academic Achievement Award, 2011-2015


🏋 Services

Conference Organizing Committee

External Reviewer for Funding Proposals

Journal Reviewer

Program Committee and/or (Meta-)Reviewer for Conferences and Workshops


👑 Automation, System, and APplication (ASAP) Lab

Openings. I am peacefully looking for interns (from now) and Ph.D. student (from Fall 24).

Timeline for PhD Offer. We could chat now but no offer can be made prior to Mid-Jan 2024.

Email for Connection - Please email the following (subject line: "Interested in {position, e.g., Ph.D.} at {expected time, e.g., Fall 24}"): Magic Words please add Trojan in the email title to show you read this carefully.

  • CV: Provide a concise background about yourself and your plan for future steps.
  • Research experience [at least 1 relevant publication in anomaly detection, (ML) systems, and HPC is required (likely in top ML/AI/System conferences)]
  • Why ASAP?: Do share if any of my research papers or topics are of interest to you. Also, feel free to suggest new topics that you would like to explore. I am always open to fresh perspectives and ideas.

General Expectations. Strong programming is essential as our work revolves around ML Systems and open-source ML. A solid foundation in statistics and mathematics will be a significant plus. Don't fear making mistakes; they're a part of the learning curve in both research and life.

Compensation. Ph.D. students will receive the full support (tuition waiver + stipend is $40,000 in 2023-24) outlined by the CS department. We do not have paid opportunities for undergraduate and master RA; it is not allowed to pay remote interns at USC :( Please consider this before reaching out.