Hi there, I'm Yue ZHAO (่ตต่ถ in Chinese)! ๐
Contributions to 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). My work has been widely used by thousands of projects, including leading firms like IBM, Morgan Stanley, and Tesla. See more applications.
| Primary field | Secondary | Method | Year | Venue | Lead author |
|---|---|---|---|---|---|
| large-scale Benchmark | anomaly detection | ADBench | 2022 | Preprint | Y |
| 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 | semi-supervised | XGBOD | 2018 | IJCNN | Y |
| ensemble learning | LSCP | 2019 | SDM | 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 | large-scale Benchmark | HR manage. | 2018 | Intellisys | Y |
| AI x Science | CIBS | 2020 | BIBM | ||
| AI x Science | PyHealth | 2020 | Preprint | Y | |
| AI x Science | large-scale 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.
- PyOD: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
- ADBench: The most comprehensive tabular anomaly detection benchmark (30 anomaly detection algorithms on 55 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.)!
- PyTorch Geometric (PyG): Graph Neural Network Library for PyTorch. Contributed to profiler & benchmarking, and heterogeneous data transformation, as a member of the PyG team.
- Python Graph Outlier Detection (PyGOD): A Python Library for Graph Outlier Detection.
- Therapeutics Data Commons (TDC): Machine learning for drug discovery.
- 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).
- Email (zhaoy [AT] cmu.edu)
- ็ฅไน:ใๅพฎ่ฐใ
- ๅพฎไฟก @ ้ฟ่ฐ
- Homepage
& Travel:
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Jun 2022: We just released a 36-page, the most comprehensive anomaly detection benchmark paper. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 55 benchmark datasets. Please star, fork, and follow for the latest update! See paper here!
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Jun 2022: Have a new system out TOD: GPU-accelerated Outlier Detection via Tensor Operations. with George H. Chen and Zhihao Jia. Preprint, Code being released
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TOD is the first fast, comprehensive, GPU-based outlier detection system.
๐ on average it is 11 times faster than PyOD!๐ it supports various OD algorithms, e,g., kNN, LOF, ABOD, HBOS, etc.
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Jun 2022:
๐ Reached 900 citations on Google Scholar! -
May 2022: Invited to present at Morgan Stanley for automated outlier detection!
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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!