My name is Yue ZHAO (赵越 in Chinese). I am a Ph.D. student at Carnegie Mellon University (CMU), and an ex management consultant at PwC Canada. I am a technical writer at Zhihu with 150,000 followers and more than 15M article reads. As a seasoned ML software/system architect, I have led/participated > 10 ML libraries initiatives, 10,000 GitHub stars (top 0.002%: ranked 800 out of 40M GitHub users), and >300,0000 total downloads. Popular ones:

  • [JMLR] PyOD: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
  • [MLSys] SUOD: An Acceleration System for Large-scale Heterogeneous Outlier Detection (Anomaly Detection).
  • TDC: An extensive machine learning data hub for therapeutic tasks (ML for Therapeutic).
  • MetaOD: Automating Outlier Detection via Meta-Learning (Anomaly Detection).
  • PyHealth: A Python Library for Healthcare AI (ML for Healthcare).
  • [AAAI] combo: A Python Toolbox for ML Model Combination (Ensemble Learning).
  • [AAAI] TODS: Time-series Outlier Detection System. Contributed to core detection model.

My research focuses on three independent but interleaved streams:

  • data mining topics related to outlier detection (anomaly detection)
  • machine learning systems (MLSys) that can speed up and/or scale up data mining and machine learning algorithms

At CMU, I work with Prof. Leman Akoglu (Heinz) on anomaly detection, and Prof. Zhihao Jia (CSD) on machine learning systems (MLSys).

General Notes: I am open to ML/DM Internship (2022). Please reach out :)

Contact me by Email (zhaoy [AT] or WeChat (微信) @ yzhao062.

[#1] I am open to collaboration opportunities (anytime & anywhere) and research internships (summer 2021/2022). 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 (e.g., Stanford, Havard, Facebook).

[#2] Call for review oppt. I am looking for paper review, tutorial, workshop, and talk opportunities (in anomaly detection, scalable ML, machine learning systems, AutoML, information systems, and ensemble learning).

[#3] I host a WeChat group on anomaly detection (异常检测微信讨论组), along with more than three hundred of researchers (e.g., Berkley, MIT, Tsinghua, etc.) and industry people (e.g., Alibaba, IBM, Facebook, etc.) for collaboration and intern/full-time opportunities. Ping me to join!

Word Cloud from Paper Titles


  • Outlier & Anomaly Detection
  • Machine Learning Systems (MLSys)
  • Automated Machine Learning
  • Scalable Machine Learning
  • Healthcare AI & Therapeutic for ML
  • Ensemble Learning
  • Clustering
  • Active Learning
  • Information Systems


  • Ph.D. in Machine Learning and Information Systems, 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


News & Travel

Mar 2020: (confirmed!) I will join Prof. Jure Leskovek‘s team @ Stanford University for a summer research intern:)

Feb 2021: Therapeutics Data Commons (TDC), a large collection of > 60 machine learning-ready datasets across more than 20 therapeutic tasks, is released. See paper on arxiv! Great work led by Kexin Huang and Prof. Marinka Zitnik from Havard!

Jan 2021: Have a new system paper (SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection) accepted at Conference on Machine Learning and Systems (MLSys). SUOD is an acceleration system for large-scale unsupervised outlier detection with Xiyang Hu. It has been downloaded by more than 900,000 times, included as part of PyOD.

Jan 2021: We have a new library PyHealth released for more than 30 state-of-the-art predictive health algorithms (mostly deep learning based). See the corresponding paper as well!

Jan 2021: Invited talk by University of Nottingham on general ML applications and career development. Link to be shared soon! See my previous talks.

Fun Facts

[#1] I am a dedicated writer with more than 200 articles (in Chinese) and 140,000 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 (微调).

Profile & Casual Pictures



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.

Journal/Conference Reviewer

Program Committee


Anomaly Detection Algorithms, Applications, and Systems (in Chinese)



See my Google Scholar, DBLP, ORCID, and ResearchGate.

Prepints & Working Papers

[w21b] Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics, with Kexin Huang, Tianfan Fu, Wenhao Gao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik. Preprint.

[w21a] PyHealth: A Python Library for Health Predictive Models, with Zhi Qiao (equal contribution), Cao (Danica) Xiao, Lucas M. Glass, and Jimeng Sun. Preprint.

[w20i] Automating Outlier Detection via Meta-Learning, with Ryan A. Rossi and Leman Akoglu. Submitted to a major CS conference, under review. Preprint.

Peer-reviewed Papers

(2020). SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources. IEEE International Conference on Data Mining Workshops (ICDMW).

PDF Code

(2020). Combining Machine Learning Models Using combo Library. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), demo track.

PDF Code Video DOI

(2020). SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula. Workshops at the Thirty-Fourth AAAI Conference on Artificial Intelligence.

PDF Code PPAI Arxiv

(2018). DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Workshop on Outlier Detection De-constructed (ODD).

PDF Poster Slides

(2017). An empirical study of touch-based authentication methods on smartwatches. Proceedings of the 2017 ACM International Symposium on Wearable Computers (Equal contribution).



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!


SUOD Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection


A Python Toolbox for Machine Learning Model Combination.

Python Outlier Detection Toolbox

PyOD–A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).


Professional Positions


Machine Learning Research Intern

IQVIA, Analytics Center of Excellence

May 2020 – Aug 2020 Boston, MA, USA

Designed new machine learning systems and models in healthcare.

Supervised by Dr. Cao (Danica) Xiao (IQVIA) and Prof. Jimeng Sun (UIUC).


Senior Consultant

PwC Canada, Consulting & Deals

Feb 2017 – Jun 2019 Toronto, ON, Canada
I was a senior consultant with the following duties:

  • Designed fraud analytic solutions for major Canadian banks and insurance firms.
  • Led applied data analytics projects, e.g., client segmentation and churn analysis.
  • Developed multiple pricing optimization models with statistical methods.

Research Associate (Intern)

PwC Canada, Consulting & Deals

May 2016 – Dec 2016 Toronto, ON, Canada

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).


Software Engineer (Contract & Intern)

Siemens PLM Software USA

Mar 2012 – Dec 2014 Cincinnati, Ohio, USA
As a co-op student and contractor, my works include:

  • Managed a Java project to transition the LabManager system to vCloud Director.
  • Refactored outdated automation code and added new modules and JUnit test cases.
  • Led a C++ Code Coverage project on Teamcenter platform to strengthen its stability.


Teaching Positions


Teaching Assistant

Carnegie Mellon University, Heinz College of Information Systems and Public Policy

Feb 2021 – May 2021 Toronto, ON, Canada
I am a teaching assistant for Intro to Artificial Intelligence taught by Prof. David Steier. Grading assignments and giving lectures on selected topics.

Teaching Assistant

Carnegie Mellon University, Heinz College of Information Systems and Public Policy

Sep 2020 – Dec 2020 Toronto, ON, Canada
I am a teaching assistant for Intro to Artificial Intelligence taught by Prof. David Steier. Grading assignments and giving lectures on selected topics.

Teaching Assistant

University of Toronto, Department of Computer Science

Sep 2015 – Dec 2015 Toronto, ON, Canada
I was a teaching assistant for Embedded Systems taught by Prof. Philip Anderson.

Teaching Assistant

University of Cincinnati, Department of Electrical Engineering & Computer Science

Sep 2014 – Dec 2014 Cincinnati, OH, USA
I was a teaching assistant for Introduction to Programming taught by Prof. George Purdy.

Funds and Awards

CMU GSA/Provost Conference Funding

Part of the travel grant for attending ICDM 2020.

AAAI Student Travel Grant & CMU GSA/Provost Conference Funding

Part of the travel grant for attending AAAI 2020.

Mitacs-Accelerate Research and Development Funding

Project IT07884 ($30,000): machine learning in HR analytics.

Mantei/Mae Award & Scholar

Awarded to highest-performing students in Electrical Engineering, Computer Engineering, and Computer Science ($40,000 in four years).

University Global Award and Scholarship

Awarded to top performing international students ($32,000 in four years).