Biography
I am a Ph.D. student at H. John Heinz III College, Carnegie Mellon University. I am advised by Prof. Leman Akoglu and Prof. Amelia Haviland. My research focuses on data mining algorithms, systems, applications, and implications to decision process and policy-making:
- proposing fundamental algorithms to tackle complex problems, including outlier & anomaly detection, ensemble learning, and clustering.
- designing scalable machine learning systems for optimized performance.
- marrying data mining with other areas (such as healthcare and finance) to build applications and understand their implications.
I am an active software developer with more than 4000 GitHub stars in total (top 1,500 among 37,000,000 GitHub developers ranked by Gitstar Ranking). I led multiple popular open-source machine learning initiatives, including pyod (total downloads > 100,000 times), combo, anomaly-detection-resources, and awesome-ensemble-learning. Before coming to CMU, I have more than 5-year industry experience as a software engineer and management consultant. See my professional experience for more information.
Interests
- Outlier & Anomaly Detection
- Ensemble Learning
- Clustering
- Outlier Ensembles
- Machine Learning Tools & Systems
- Machine Learning Applications
Education
-
Ph.D. in Information System & Management (Primary); Joint Ph.D. in Machine Learning & Public Policy (Expected), 2019-2024
Carnegie Mellon University
-
M.S. in Applied Computing, 2016
University of Toronto
-
B.S. in Computer Engineering, 2015
University of Cincinnati
-
High School Diploma, 2010
Shanxi Experimental Secondary School (山西省实验中学)
