Biography

📰 I am on the market with expected graduation in Summer 2023. I am broadly interested in machine learning, data mining and science, and information science and systems positions. I can work in the U.S., Canada, and China without sponsorship; please reach out if you have an open opportunity in either academia or industry! Contact me by Email (zhaoy [AT] cmu.edu) or WeChat (微信) @ yzhao062.

Who am I (赵越)? I am a 4-th year Ph.D. student at Carnegie Mellon University (CMU). Before joining CMU, I earned my Master degree from University of Toronto (2016) and Bachelor degree from University of Cincinnati (2015), and worked as a senior consultant at PwC Canada (2016-19). I am an expert on anomaly detection (a.k.a outlier detection) algorithms, systems, and its applications in security, healthcare, and Finance, with more than 7-year professional experience and 20+ papers (in JMLR, TKDE, NeurIPS, etc.). I appreciate the support from Norton Labs Graduate Fellowship.

At CMU, I work with Prof. Leman Akoglu, Prof. Zhihao Jia, and Prof. George H. Chen. I am a member of Data Analytics Techniques Algorithms (DATA) Lab and CMU automated learning systems group (Catalyst). Externally, I collaborate with Prof. Jure Leskovec at Stanford, Prof. Xia “Ben” Hu at Rice University, and Prof. Philip S. Yu at UIC.

Contributions to outlier detection systems, benchmarks, 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). To understand the characteristics of OD algorithms, I co-author large-scale benchmarks for tabular data (ADBench), time-series data (paper), and graph data (UNOD). My work has been widely used by thousands of projects and applications, including firms like IBM, Morgan Stanley, and Tesla. See more applications.

Research outcomes (primarily for outlier detection if not specified):

Open-source Contribution: I have led or contributed as a core member to more than 10 ML open-source initiatives, receiving 14,000 GitHub stars (top 0.002%: ranked 800 out of 40M GitHub users) and >10,000,000 total downloads. Popular ones:

• PyOD: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
• ADBench: The most comprehensive tabular anomaly detection benchmark (30 anomaly detection algorithms on 57 benchmark datasets).
• TOD: Tensor-based outlier detection–First large-scale GPU-based system for acceleration!
• SUOD: An Acceleration System for Large-scale Heterogeneous Outlier Detection.
• anomaly-detection-resources: The most starred resources (books, courses, etc.)!
• Python Graph Outlier Detection (PyGOD): A Python Library for Graph Outlier Detection.
• Therapeutics Data Commons (TDC): Machine learning for drug discovery.
• PyTorch Geometric (PyG): Graph Neural Network Library for PyTorch. Contributed to profiler & benchmarking, and heterogeneous data transformation.
• combo: A Python Toolbox for ML Model Combination (Ensemble Learning).
• TODS: Time-series Outlier Detection. Contributed to core detection models.
• MetaOD: Automatic Unsupervised Outlier Model Selection (AutoML).

[#1] 我组织并维护多个机器学习研究社交微信群，包括

• anomaly detection (异常检测微信讨论组)
• machine learning systems (机器学习系统讨论组),
• 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).
• 其他机器学习研究方向群 Join them by scanning 微信 @ 加群小助手!

[#2] I am a dedicated writer with more than 300 articles (in Chinese) and 190,000 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).