Assistant Professor of Computer Science
- 2023, Doctoral Degree, Carnegie-Mellon University
- 2016, Master's Degree, University of Toronto
- 2015, Bachelor's Degree, University of Cincinnati
Yue obtained his Ph.D. degree from Carnegie Mellon University in four years, with the support from Norton Fellowship and CMU Presidential Fellowship. He was a research intern at Norton Lab, Microsoft Research, and IQVIA, a visiting scholar at Stanford, and a senior data scientist at PwC Canada.
I build fast and automated machine learning (ML) and data mining (DM) systems, with a focus on but not limited to graph neural networks and anomaly detection.
1. Accelerate large-scale learning tasks by leveraging ML systems techniques.
2. Automate unsupervised ML by model selection and hyperparameter optimization.
3. Develop open-source ML tools to support applications in healthcare, finance, and security.
Research Keywords: Unsupervised ML, ML Systems, Automated ML, Anomaly/Outlier/Out-of-Distribution (OOD) Detection, AI for Science, Graph Neural Networks.
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