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Research Overview
I am currently campaigning against the notion of statistical significance and criticizing its covert contribution to the exploding number of misinformation claims.
My initial research interest was dimension reduction. In the early years of my faculty career, I devoted much attention to efficient kernel machines for rare target detection and ensemble methods for variable selection. I also worked on algorithms for making personalized recommendations and applications of machine learning to healthcare informatics.
While ensemble learning continued to captivate my curiosity, in more recent years I explored many different topics—including evaluation metrics, protein structures, transactional networks, genetic epistasis, dependence modeling, large covariance matrices and generative neural networks. I also taught a crash course on statistics for data science students and wrote a textbook based on the first half of it.
(last updated 2026)