I am an assistant professor in the Computer Science and Engineering Department at the University of Connecticut. I graduated from the University of Rhode Island with B.S. degrees in Computer Engineering and Computer Science, received my Ph.D. in Computer Science from Brown University, advised by Professor Sorin Istrail, and completed my postdoctoral work at Princeton University with Professor Barbara Engelhardt. My research aims to develop probabilistic machine learning models, combinatorial algorithms, and scalable inference methods to better understand high-dimensional data, particularly genomics and genetics data applied to complex disease.

I am looking for motivated PhD students interested in research at the intersection of probabilistic machine learning and algorithms and applying this work to application areas including genomics, transcriptomics, population genetics, sociology, and law.

Highlights

HapCompass

HapCompass is a state-of-the-art haplotype assembler presented at ISMB 2013 and published in Bioinformatics and the Journal of Computational Biology. It is extremely accurate and efficient and the only haplotype assembly algorithm capable of polyploid haplotype assembly.

BIISQ

BIISQ is a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. BIISQ does not require isoform reference sequences but instead estimates an isoform catalog shared across samples.

CSE5095 Bayesian ML

In Bayesian Machine Learning, we cover the three fundamental components of Bayesian Machine Learning: probabilistic modeling, inference algorithms, and model checking.

Explore Engineering

Explore Engineering is a one-week STEM summer camp for high schoolers where they are introduced to engineering. Students who choose the CSE track are introduced to Programming, Data Science, Machine Learning and Artificial Intelligence.