Yuval Kluger appointed Anthony N. Brady Professor of Pathology

Kluger works in the broad fields of bioinformatics, machine learning, and applied mathematics to develop approaches for analyzing large biological data sets.
Yuval Kluger
Yuval Kluger

Yuval Kluger, who works in the broad fields of bioinformatics, machine learning, and applied mathematics to develop new approaches for analyzing large biological data sets, was recently appointed the Anthony N. Brady Professor of Pathology.

Kluger completed his Ph.D. in theoretical physics at Tel Aviv University and postdoctoral training in physics at the Los Alamos and Berkeley National Laboratories. He was a staff member in theoretical physics at the Los Alamos National Laboratory before he came to Yale, where he transitioned into the field of computational biology. In 2003, he joined the faculty at New York University in the Department of Cell Biology. In 2010, he was recruited to the Department of Pathology at Yale School of Medicine. He also has an appointment in the Program of Applied Mathematics at Yale.

His research centers on developing machine learning, mathematical, and statistical approaches for analyzing genomics and proteomics data. In response to the urgent need for new analytic tools to keep up with evolving technologies for profiling biological specimens, Kluger’s research group is working to develop sophisticated strategies for signal detection and for denoising, clustering, visualizing, compressing and integrating data generated from emerging technologies. He and his team has worked on problems of identifying cell type specific regulatory networks, de-mixing signals from a pool of heterogeneous cell populations, and constructing predictive biomarker models in high dimensional assays. He and his team has developed novel spectral analysis- based methods for denoising raw data from sequencing modalities, for two-way clustering of large gene by sample datasets, for selecting the best algorithm for detecting signals in new datasets, for phylogeny and for extracting patterns hidden in subspaces of multiplexed datasets. In particular, his team developed scalable algorithms of linear and nonlinear dimensional reduction techniques to compress and visualize very large genomics datasets; new multi-scale graphical and artificial intelligence methods for differential analysis tasks aimed at detecting biomarkers associated with a phenotype or disease state; and tree-based phylogenetic models to study cancer progression. For spatial omics technologies, which are key for analyzing tumor or tissue specimens, his team developed new signal analysis graph-based tools for diagnostics.

Kluger’s research has been supported by continued funding from the National Institutes of Health, the U.S. Department of Defense, and several foundations. His work has been publihsed in high impact journals and presented at top artificial intelligence conferences.

In recognition of his scholarship and achievements, Kluger was selected for the Minerva Fellowship in Theoretical Physics, the Sloan and Department of Energy Fellowship in Computational Molecular Biology, and a courtesy appointment at the Courant Institute of Mathematical Science. He served as a board member for the Oak Ridge Scientific Advisory Committee for the Center of Molecular and Cellular Systems. He has served on numerous NIH and National Science Foundation study sections and was a permanent member of the NIH Cancer Biomarkers Study Section.

Given his unique academic background, he was invited to organize activities in the interface between computational biology, mathematics and physics, including programs at the Institute for Pure and Applied Mathematics (IPAM), and the Nordic Institute for Theoretical Physics.  Six postdoctoral fellows and students from his team have obtained assistant professor positions in departments of mathematics, statistics, computer science, engineering and health informatics.

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