Hi there, I'm Yue ZHAO (赵越 in Chinese)! 👋
I am a Ph.D. student at Carnegie Mellon University (CMU). I am also a seasoned machine learning (ML) software/system architect with 10 ML libraries, 8,000 GitHub stars (top 0.002%: ranked 820 out of 40M GitHub users), and >200,0000 total downloads.
Good news: I am looking for 2021 Summer ML/DM Internship in Canada, United States, or China. Not necessarily pure research; system or AutoML related stuff would be great fit as well. Just reach out and we could work something out :)
And of course, I am still a ML/DM researcher at the end of the day.
- data mining topics related to scalability, reliability, and automation and
- information systems questions related to interaction, trade-off, and cooperation between human and “AI”
- collaboration opportunities (anytime & anywhere & any type) and
- research internships (open for Summer 2021). I could legally work in Canada, United States, and China
- Email (zhaoy [AT] cmu.edu)
- 知乎:「微调」
- WeChat (微信)
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Sep 2020: We have a new paper Automating Outlier Detection via Meta-Learning (code) out. In this paper, we propose the first unsupervised meta-learner that can select (recommend) the most performing outlier detection model on an arbitrary dataset.
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Sep 2020: Our new library TODS, a Python library for time-series outlier detection is out. It is initialized and led by DATA Lab @ Texas A&M University, and I contribute to core detection model design and implementation. See the paper and video under submission @ AAAI 2021 (demo).
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Sep 2020: Our paper COPOD: Copula-Based Outlier Detection (camera-ready version) will appear in ICDM 2020 soon! It is a fast, parameter-free, and highly interpretable unsupervised outlier detection algorithm and available in PyOD now.
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Sep 2020: Have a paper accepted at ICDM Workshops 2020 (ICDMW). A personal copy can be found here: SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources.
