Applications:
Bioinformatics, Learning Health Systems, Medical Imaging, Medical Informatics, Mental Health
Methodologies:
High-Dimensional Data Analysis, Machine Learning, Pattern Analysis and Classification, Statistical Inference, Statistical Modeling, Statistics
Relevant Projects:

NSF, NIH


Kean Ming Tan

Assistant Professor

Department of Statistics

I am an applied statistician working on statistical machine learning methods for analyzing complex biomedical data sets. I develop multivariate statistical methods such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction to uncover patterns from massive data set. Recently, I also work on topics related to robust statistics, non-convex optimization, and data integration from multiple sources.