About Me

I am an Associate Professor in the Department of Statistics at the University of Connecticut. I received my Ph.D. in Statistics from Cornell University in 2012. Prior to academia, I worked at Motorola (Israel), MicroPatent (now Clarivate), and ATC-NY.

My research interests lie at the intersection of statistical methodology and applications in biology, genomics, and medicine. I develop empirical Bayes and mixture-model approaches for high-dimensional inference, variable selection in generalized linear models, graphical models, and quantile regression. Applied work spans microarray and RNA-seq analysis, codon usage, gene network inference, and clinical biostatistics.

I am affiliated with the Institute for Systems Genomics (ISG), the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), and the CT Institute for the Brain and Cognitive Sciences (IBACS) at UConn.

Education

• Cornell University — M.Sc., Ph.D. in Statistics (2012)
• Yale University — M.Sc. in Computer Science
• The Hebrew University, Jerusalem — B.Sc. in Mathematics, Cum Laude

Research Interests

• Empirical Bayes methods and mixture models
• Variable selection in high-dimensional regression
• Graphical models and network inference
• Quantile regression
• Multiple testing and false discovery rate control
• Statistical genomics and bioinformatics
• Reproducible research (literate programming with LaTeX)

Recent News

• "High Dimensional Space Oddity" The American Statistician (2025). DOI
• "Uncovering position-specific patterns in codon and codon-pair usage in candidate genes associated with blood coagulation diseases" in NAR Genomics and Bioinformatics (2025). DOI
• "A Time-Varying Branching Process Approach to Model Self-Renewing Cells" in Methodology and Computing in Applied Probability (2026). DOI