My name is Yue ZHAO (赵越 in Chinese). I am pursuing a Ph.D. in Machine Learning and Public Policy (expected) at Carnegie Mellon University. At CMU, I have been fortunate to work with Prof. Leman Akoglu and Prof. Pedro Ferreira. I focus on:
[#1] I am open to collaboration opportunities (anytime & anywhere) and research internships (open for Summer 2021). I could legally work in United States (CPT), Canada (permanent residency), and China (permanent residency). I have been working with the professionals from both industry and academia (U Toronto, UIUC, Texas A&M University, Tsinghua U, Purdue University, Northeastern U, IQVIA, Adobe, PwC, Arima, etc.).
[#2] I am actively looking for paper review, tutorial, workshop, and talk opportunities (in anomaly detection, AutoML, ensemble learning, scalable ML, and learning systems).
[#3] I host a WeChat group on anomaly detection (异常检测微信讨论组), along with more than a hundred of researchers (e.g., Berkley, Tsinghua, etc.) and industry people (e.g., Alibaba, IBM, Faceboook, etc.) for collaboration and intern/full-time position opportunities. Ping me to join!
[#4] In addition to develop the most popular outlier detection toolbox PyOD, I am also maintaining a knowledge repository for anomaly detection resources for related books, papers, videos, and toolboxes. Check out to know more about the field!
Ph.D. in Machine Learning and Public Policy (expected), 2019-2024
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
Jul 2020: Congrats to many of my great friends and fellows who become partners and principals at PwC Canada and US in 2020! Applause to David P., Jon Wong, and Marie K.!
Jul 2020: PyOD has been downloaded by more than 1,000,000 times!
Jun 2020: Busy with multiple AutoML projects. Have three papers submitted to CIKM and ICDM. Finger crossed!
May 2020: Have a new system paper (SUOD: A Scalable Unsupervised Outlier Detection Framework) ready to submit. SUOD is an acceleration system for large-scale unsupervised outlier detection. It has been downloaded by more than 300,000 times, and presented in AAAI Workshop on Artificial Intelligence for Cyber Security (AICS).
[#1] I am an active software/system developer with more than 7,000 GitHub stars in total (top 1,000 among 37,000,000 GitHub developers ranked by Gitstar Ranking). I led multiple popular open-source ML initiatives, including PyOD, combo, SUOD, anomaly-detection-resources, and awesome-ensemble-learning.
[#2] I am a dedicated technical writer with more than 200 articles (in Chinese) and 130,000 followers on Zhihu (知乎) — Chinese Quora (200 million+ registered users). Since 2018, I have been officially recognized as a “Top Zhihu Writer” (优秀回答者) in four fields (AI, ML, DM, and STAT). My articles have been read by more than 10,000,000 times with 100,000 upvotes (statistics provided by Zhihu). See my Zhihu page.
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.
[04/17/2020; Pittsburgh, PA] I will present “Developing Python Libraries for Machine Learning: Best Practices and Lessons Learned” at The Python Conference (PyCon) 2020, the largest annual gathering for the Python community.
[w20g] SALINE: A Scalable and Flexible System for Machine Learning in Healthcare, with Zhi Qiao, Cao (Danica) Xiao, and Jimeng Sun. To be submitted to JMLR (MLOSS track).
[w20f] A Statistical Based Approach for Synthetic Data Generation (Name masked due to the double-blind policy), with Zheng Li, Jialin Fu. Submitted to a major data mining conference, under review.
[w20e] A Statistical Based Approach for Outlier Detection (Name masked due to the double-blind policy), with Zheng Li, Nicola Botta, Cezar Ionescu, Xiyang Hu. Submitted to a major data mining conference, under review.
[w20f] A Cell Clustering Paper (Name masked due to the double-blind policy), with Changlin Wan, Dongya Jia, Wennan Chang, Sha Cao, Xiao Wang, Chi Zhang. Submitted to a major data mining conference, under review.
[w20b] A New Semi-supervised Anomaly Detection Model (Name masked due to the double-blind policy), with Cheng Cheng (co-first author)
and Xiyang Hu, Cao (Danica) Xiao (IQVIA), Yunlong Wang (IQVIA),
Prof. Jimeng Sun (UIUC) and Prof. Jeremy C. Weiss.
To be submitted to a major data mining conference.
[w20a] SUOD: An Acceleration System for Large-Scale Unsupervised Outlier Detection,
with Xiyang Hu, Cheng Cheng, Cong Wang (CMU & Tsinghua U), Changlin Wan (Purdue U)
Cao (Danica) Xiao (IQVIA), Yunlong Wang (IQVIA),
Prof. Jimeng Sun (UIUC) and Prof. Leman Akoglu.
Accepted in AAAI 2020 Workshop; to be submitted.
[w20e] DNA: Differentiating Noise from Anomaly by Generative Models.
[w20f] Improving Supervised Anomaly Detection via Unsupervised Representation Learning.
I am happy to give talks on the series of tools I built, e.g., PyOD, combo, and SUOD. I am also willing to share my experience as a ML developer and researcher, especially on how to build ML tools from design. Please drop me a line for invite :)
I am an enthusiastic open-source developer: I build machine learning libraries and systems. Specifically, I initialized Python Outlier Detection library (PyOD) in 2018, which has become the most popular Python outlier detection toolkit. I also initialized combo: A Python Toolbox for Machine Learning Model Combination in July 2019–it is currently under active development.
I am currently working on a new ML system called SUOD (Scalable Unsupervised Outlier Detection), for accelerating model training and prediction when a large number of outlier detectors are presented on large, high-dimensional datasets. Watch/Star/Follow welcome!
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).