News from CCMB
CCMB postdocs and students attend 2024 TAGC
In March, CCMB postdocs and students attended and shared their research at the The Allied Genetics Conference in Washington DC.
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Data Science Insitute
Center for Computational Molecular Biology
Sohini Ramachandran
Director of the Data Science Institute, Hermon C. Bumpus Professor of Biology and Data Science, Professor of Computer Science
Graduate Trainer in
EEOB, Comp Bio, CS, MCB
Biography
Professor Ramachandran addresses problems in evolutionary theory and human population genetics. The Ramachandran Lab uses mathematical modeling, applied statistical methods, and computer simulations to make inferences about evolutionary history from present-day samples of genomes. They particularly focus on developing new methods for identifying adaptive genomic elements, characterizing genetic pathways underlying common diseases, and determining features of human population histories from genetic data. Professor Ramachandran is an Alfred P. Sloan Research Fellow, a Pew Scholar in the Biomedical Sciences, and the recipient of both an NSF CAREER award and multiple grants from the National Institutes of Health.
Publications
Recent News
The COBRE Center for Computational Biology of Human Disease is advancing the use of computational tools among biomedical scientists at Brown, helping them unlock new insights that could ultimately benefit patients.
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News from DSI
Officially Announcing DSI Director Sohini Ramachandran
About one year ago, DSI announced a new interim director, Sohini Ramachandran. Today, we are happy to announce that Professor Ramachandran is officially Director of the Data Science Initiative. Below is Provost Locke's email announcement.
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News from CCMB
Prof. Ramachandran named Interim Director of the DSI
Data Science Initiative Interim Director
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Faculty members Sohini Ramachandran and Anita Shukla are among the winners of the Presidential Early Career Award in Science and Engineering, the nation’s top honor for early-career scientists and engineers.
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News from Brown
New software helps detect adaptive genetic mutations
Brown University researchers have developed a new machine learning technique that can track down beneficial mutations in population genetic datasets.
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