Hi there, I'm Yue ZHAO (赵越 in Chinese)! 👋

I am a Ph.D. student at Carnegie Mellon University (CMU), and an ex management consultant at PwC Canada. As a seasoned ML software/system architect, I have led/participated > 10 ML libraries initiatives, 10,000 GitHub stars (top 0.002%: ranked 800 out of 40M GitHub users), and >300,0000 total downloads.

🔭 Reseach-wise, I mainly focus on:

• data mining topics related to outlier detection (anomaly detection)
• machine learning systems (MLSys) that can speed/scale upp and automate data mining and machine learning algorithms

😄 I am open to:

• collaboration opportunities (anytime & anywhere & any type) and
• research internships (open for Summer 2022). I could legally work in Canada, United States, and China

📫 Contact me by:

• Email (zhaoy [AT] cmu.edu)
• Twitter
• 知乎:「微调」
• Homepage
• WeChat (微信)

💬 News & Travel:

• Apr 2021: How to evaluate/select outlier detection models without any external information (e.g., ground truth)? We have a new preprint on using internal strategies for model selection. Do they suffice? Check out our paper!

• Mar 2020: I will join Prof. Jure Leskovek's team @ Stanford University during summer :)

• Feb 2021: Therapeutics Data Commons (TDC), a large collection of > 60 machine learning-ready datasets across more than 20 therapeutic tasks, is released. See paper on arxiv! Great work led by Kexin Huang and Prof. Marinka Zitnik from Havard!

• Jan 2021: Have a new system paper (SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection) accepted at Conference on Machine Learning and Systems (MLSys). SUOD is an acceleration system for large-scale unsupervised outlier detection with Xiyang Hu. It has been downloaded by more than 900,000 times, included as part of PyOD.

• Jan 2021: We have a new library PyHealth released for more than 30 state-of-the-art predictive health algorithms (mostly deep learning based). See the corresponding paper as well!