Hi there, I'm Yue ZHAO (赵越 in Chinese)! 👋
I am a third-year Ph.D. student at Carnegie Mellon University (CMU), and an ex management consultant at PwC Canada. I have led/participated > 10 ML open-source initiatives, receiving 11,000 GitHub stars (top 0.002%: ranked 900 out of 40M GitHub users) and >400,0000 total downloads.
I specialize in designing and building machine learning systems, with realization and applications in outlier detection, healthcare, graph neural networks, and ensemble learning.
- data mining topics related to outlier detection (anomaly detection)
- machine learning systems (MLSys) that can speed/scale up and automate data mining and machine learning algorithms
At CMU, I work with Prof. Leman Akoglu from DATA Lab on outlier detection, Prof. George H. Chen on general ML and statistics, and Prof. Zhihao Jia from Catalyst on machine learning systems. I am currently visiting Prof. Jure Leskovec at SNAP, Standford University.
- collaboration opportunities (anytime & anywhere & any type)
- paper review, tutorial, workshop, and talk opportunities
- research internships (open for Summer 2022). I could legally work in Canada, United States, and China
- Email (zhaoy [AT] cmu.edu)
- 知乎:「微调」
- Homepage
- WeChat (微信)
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Aug 2021: Two impactful large-scale ML initiatives are accepted to NeurIPS 2021 (Datasets and Benchmarks track). See the papers on OpenReview: (1) Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development and (2) Revisiting Time Series Outlier Detection: Definitions and Benchmarks.
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May-Aug 2021: Visiting at Standford University in SNAP by Prof. Jure Leskovec.
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May 2021: Have a new journal paper titled Copula-Based Outlier Detection under review. It is based on our ICDM’ 20 paper with more theoretical analysis. See the extended journal version!