Hi there, I'm Yue ZHAO (赵越 in Chinese)! 👋
Advisors and Collaboration. At CMU, I work with Prof. Leman Akoglu for automated data mining, Prof. Zhihao Jia for machine learning systems, and Prof. George H. Chen for general ML. I am a member of CMU automated learning systems group (Catalyst) and Data Analytics Techniques Algorithms (DATA) Lab. In addition, I have collaborated with Prof. Jure Leskovec at Stanford and Prof. Philip S. Yu at UIC on various projects.
See my homepage, CV, research statement, and teaching statement for more information.
| Primary field | Secondary | Method | Year | Venue | Lead author |
|---|---|---|---|---|---|
| large-scale Benchmark | tabular anomaly detection | ADBench | 2022 | NeurIPS | Y |
| large-scale Benchmark | graph anomaly detection | BOND | 2022 | NeurIPS | Y |
| large-scale Benchmark | sequence anomaly detection | TODS | 2021 | NeurIPS | |
| automated machine learning | outlier model selection | MetaOD | 2021 | NeurIPS | Y |
| automated machine learning | outlier model selection | ELECT | 2022 | ICDM | Y |
| automated machine learning | outlier HP optimization | HPOD | 2022 | Preprint | Y |
| automated machine learning | outlier evaluation | IPM | 2023 | KDD Explor. | Y |
| machine learning systems | PyOD | 2019 | JMLR | Y | |
| machine learning systems | time series | TODS | 2020 | AAAI | |
| machine learning systems | SUOD | 2021 | MLSys | Y | |
| machine learning systems | distributed systems | TOD | 2022 | VLDB | Y |
| machine learning systems | graph neural networks | PyGOD | 2022 | Preprint | Y |
| robust ML | semi-supervised | XGBOD | 2018 | IJCNN | Y |
| robust ML | ensemble learning | LSCP | 2019 | SDM | Y |
| robust ML | ensemble learning | combo | 2020 | AAAI | Y |
| robust ML | ensemble learning | COPOD | 2020 | ICDM | Y |
| robust ML | ensemble learning | ECOD | 2022 | TKDE | Y |
| robust ML | noisy label learning | ADMoE | 2023 | AAAI | Y |
| graph mining | finance | AutoAudit | 2020 | BigData | |
| graph neural networks | contrastive learning | CONAD | 2022 | PAKDD | |
| Diffusion Models | survey | 2022 | Preprint | ||
| AI x Science | synthetic data | SynC | 2020 | ICDMW | |
| AI x Science | healthcare AI | PyHealth | 2020 | Preprint | Y |
| AI x Science | Datasets & Benchmark | TDC | 2021 | NeurIPS | |
| AI x Science | Datasets & Benchmark | TDC V2 | 2022 | NCHEMB |
- 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.)!
- 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).
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Nov 2022: Happy to serve as the workflow co-chair for KDD 2023!
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Nov 2022: ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels will appear in AAAI 2023--the first framework of using multiple sets of noisy labels for anomaly detection.
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Oct 2022: Have a new system paper out TOD: GPU-accelerated Outlier Detection via Tensor Operations. with George H. Chen and Zhihao Jia. VLDB paper, Code.
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Oct 2022: Great news! Our proposal (led by Prof. Zhihao Jia) for AI-assisted systems has been funded via Meta 2022 AI4AI Research!
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Sep 2022: Artificial Intelligence Foundation for Therapeutic Science published in Nature Chemical Biology. The paper describes Therapeutics Data Commons (TDC) and its various use cases, laying the foundation of therapeutic science.
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Sep 2022: Two large-scale anomaly detection benchmarks for tabular data (ADBench) and graph data (BOND) accepted at NeurIPS 2022.