Haim Bar

Haim Bar is an Associate Professor in the Department of Statistics at the University of Connecticut. His research focuses on high-dimensional data analysis, bioinformatics, and statistical modeling with applications to genomics, proteomics, and public health. He leads initiatives such as the CoCoPUTs project, analyzing codon usage patterns across species and tissues, and develops statistical methods for translational medicine and precision health.

Key contributions include the creation of the CancerCoCoPUTs database for cancer-specific codon usage, work on zinc status biomarkers for nutritional interventions, and proteomic-based prediction of immunotherapy responses. His methods address challenges in variable selection, graphical models, and handling missing data in large-scale biological datasets.

Bar’s articles span statistical innovation (e.g., graphical models and convex geometry) to applied research in genetics, virology, and oncology. He collaborates across disciplines, integrating computational tools with clinical and ecological data to solve complex biological problems. His lab’s open-source tools like R-CMap advance concept mapping and data visualization.