Short Bio: My name is Yue ZHAO (赵越 in Chinese). I am a third-year Ph.D. student at Heinz College, Carnegie Mellon University (CMU). Before joining CMU, I earned my Master degree from University of Toronto, and worked as a senior consultant at PwC Canada. I have coauthored more than 20 papers (in JMLR, TKDE, NeurIPS, etc.) on anomaly detection and its applications in security and healthcare. Service-wide, I am on the conference program committee of KDD, AAAI, and IJCAI, and reviewing for JMLR, TPAMI, and TKDE. I am one of the two recipients of the 2022 Norton Labs Graduate Fellowship.
Outlier detection systems and applications: I build automated, scalable, and accelerated machine learning systems (MLSys) to support large-scale real-world outlier detection applications in security, finance, and healthcare with millions of downloads. I designed CPU-based (PyOD), GPU-based (TOD), distributed detection systems (SUOD) for tabular (PyOD), time-series (TODS), and graph data (PyGOD).
Research outcomes (related to outlier detection if not specified):
|Primary field||Secondary||Method||Year||Venue||Lead author|
|machine learning systems||PyOD||2019||JMLR||Y|
|machine learning systems||time series||TODS||2020||AAAI|
|machine learning systems||benchmark||TODS||2021||NeurIPS|
|machine learning systems||SUOD||2021||MLSys||Y|
|machine learning systems||distributed systems||TOD||2022||Preprint||Y|
|machine learning systems||graph neural networks||PyGOD||2022||Preprint||Y|
|ensemble learning||machine learning systems||combo||2020||AAAI||Y|
|ensemble learning||interpretable ML||COPOD||2020||ICDM||Y|
|ensemble learning||interpretable ML||ECOD||2022||TKDE||Y|
|automated machine learning||graph mining||AutoAudit||2022||BigData|
|automated machine learning||MetaOD||2021||NeurIPS||Y|
|graph neural networks||contrastive learning||CONAD||2022||PAKDD|
|AI x Science||benchmark||HR manage.||2018||Intellisys||Y|
|AI x Science||CIBS||2020||BIBM|
|AI x Science||PyHealth||2020||Preprint||Y|
|AI x Science||benchmark||TDC||2021||NeurIPS|
At CMU, I work with Prof. Leman Akoglu (DATA Lab), Prof. Zhihao Jia (Catalyst), and Prof. George H. Chen. Externally, I collaborate with Prof. Jure Leskovec at Stanford University and Prof. Xia “Ben” Hu at Rice University.
Open-source Contribution: I have led or contributed as a core member to more than 10 ML open-source initiatives, receiving 13,000 GitHub stars (top 0.002%: ranked 800 out of 40M GitHub users) and >8,000,000 total downloads. Popular ones:
[#1] I host a WeChat group on anomaly detection (异常检测微信讨论组) & machine learning systems (MLSys讨论组), along with more than four hundred of researchers (e.g., Berkley, MIT, Tsinghua, etc.) and industry people (e.g., Alibaba, IBM, Meta, etc.) for collaboration and intern/full-time opportunities. Join it by scan 微信 @ 加群小助手!
[#2] I host a WeChat group for ML Ph.D. (北美ML博士求职分享群) where we share postdoc, intern, and full-time jobs for ML Ph.D. (students). Join it by scan 微信 @ 加群小助手!
[#3] I am a dedicated writer with more than 300 articles (in Chinese) and 170,000 followers on Zhihu (知乎) — Chinese Quora (200 million+ registered users). I have been officially recognized as a “Top Writer” (优秀回答者) in four fields (AI, ML, DM, and STAT). My articles have been read by more than 20,000,000 times. See my Zhihu page (微调).Contact me by Email (zhaoy [AT] cmu.edu) or WeChat (微信) @ yzhao062.
Ph.D. Student in Information Systems, 2019-2023
Carnegie Mellon University
M.S. in Applied Computing, 2015-2017
University of Toronto
B.S. in Computer Engineering (Minor in Computer Science and Math), 2015
University of Cincinnati
High School Diploma, 2010
Shanxi Experimental Secondary School 山西省实验中学
May 2022: Invited to present at Morgan Stanley for automated outlier detection!
Apr 2022: 🌟 Reached 800 citations on Google Scholar!
Apr 2022: PyGOD (Python Graph Outlier Detection) received 400+ stars in a week! We released PyGOD (Python Graph Outlier Detection). With PyGOD, you could do anomaly detection with the latest graph neural networks in 5 lines! See paper here!
Mar 2022: Invited to present at Morgan Stanley for large-scale anomaly detection systems!
Mar 2022: 🎉 I received the prestigious 2022 Norton Labs Graduate Fellowship (one of the two graduate students worldwide). Thanks to the selection committee and my advisors!
Mar 2022: ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions is accepted to IEEE Transactions on Knowledge and Data Engineering (TKDE)! ECOD is a simple yet effective detection algorithm with extremely fast O(nd) runtime.
Feb 2022: Propose a new initiative called Detected AI (detected.ai) for large-scale anomaly detection applications. It is still too early to tell, but it will be exciting!
[w22a] TOD: GPU-accelerated Outlier Detection via Tensor Operations, with George H. Chen and Zhihao Jia. Under submission at a key ML conference. Preprint.
[w21c] A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice? with Martin Q. Ma (equal contribution), Xiaorong Zhang, and Leman Akoglu. Preprint.
I am open to peer review and organizing chances in the field of outlier & anomaly detection, ensemble Learning, clustering, ML libraries & systems, and information systems.
Designed new machine learning systems and models in healthcare.
Supervised by Dr. Cao (Danica) Xiao (IQVIA) and Prof. Jimeng Sun (UIUC).
Applied research in people analytics with machine learning.
Supervised by Prof. Anthony Bonner and the project is partly supported by Mitacs-Accelerate Research and Development Funding (IT07884).
I am a teaching assistant for the following courses:
The main duties include grading assignments and giving lectures on selected topics.
To find more of my open-source initiatives, see my GitHub.