Sohini Ramachandran joined the faculty of Brown University in July 2010, and was named the Hermon C. Bumpus Professor of Biology in January 2023; she also holds a courtesy faculty appointment in Computer Science. She is also the Founding Director of Brown University's Data Science Institute, and directed Data Science at Brown from August 2020 through August 2024. From July 2017-July 2022, Sohini served as Director of Brown University's Center for Computational Molecular Biology.
Prior to beginning her faculty appointment at Brown University, Sohini spent 3 years as a Junior Fellow at the Harvard Society of Fellows and postdoctoral fellow in Professor John Wakeley’s group at the Harvard University Department of Organismic and Evolutionary Biology. She completed her PhD in 2007 with Professor Marcus Feldman at Stanford University’s Department of Biological Sciences.
Sohini's research program has been recognized with multiple national awards, including a 2012 Sloan Research Fellowship, funding as a Pew Biomedical Scholar (2012-2016), and a Presidential Early Career Award in Science and Engineering (PECASE, nominated by the NIH, awarded 2019). She is also the Contact PI and Program Director of Brown University's NIH T32 Predoctoral Training Program in Biological Data Science, and a standing member of the NIH Genetic Variation and Evolution study section (7/2018-6/2024). In Spring 2019, Sohini was a Natural Sciences Fellow at the Swedish Collegium for Advanced Study in Uppsala, Sweden. In recognition of her teaching at Brown University, she has received a Henry Merritt Wriston Fellowship (2016) and the Philip J. Bray Award for Excellent in Teaching in the Physical Sciences (2021).
Sohini is an avid knitter, and loves running in Providence with her colleagues Emilia Huerta-Sanchez and Ritambhara Singh. She seeks out infinite games, and her self-competitive spirit is fueled by having misspelled 'succedaneum' at the 1995 National Spelling Bee.
Current CV and Google Scholar citations
Xiran Liu completed her Ph.D. in Computational and Mathematical Engineering at Stanford University in 2023. She worked with longtime lab collaborator Dr. Noah Rosenberg on topics in population genetics, evolutionary biology, and computational biology. Her dissertation focused on developing computational methods and mathematical measures for understanding population relationships. Prior to her Ph. D., she earned dual bachelor’s degrees in Systems Science and Engineering and Computer Science from Washington University in St. Louis. Her primary interest lies in devising data-oriented approaches for genetics and evolutionary studies. She is interested in understanding the information provided by genetic data on various problems including association analysis, clustering, inference, and prediction. She also wants to integrate interpretability into learning models used to analyze genetic and genomic data.
Alex Diaz-Papkovich completed his PhD in Quantitative Life Sciences in 2023 at McGill University in Canada. He was supervised by Simon Gravel and developed methods of dimensionality reduction, clustering, and visualization of population structure in biobanks. Prior to his PhD, Alex worked as a methodologist at Statistics Canada while working towards his MSc in Probability & Statistics from Carleton University, with a thesis on data mining for hockey analytics. He holds a BMath from the University of Waterloo in Statistics and Pure Mathematics. His main research interests at the Ramachandran Lab are studying the interplay between genetic, phenotypic, environmental, geographic, and other types of data in large and complex biobanks, as well as studying how genetics research lives in the information ecosystem so as to mitigate its weaponization.
Elsie graduated from Harvey Mudd College in 2015 with a B.S. in Mathematical and Computational Biology, where she worked with Lauren Chan. Prior to joining the Ramachandran Lab during Fall 2019, Elsie worked as an ORISE intern at the Walter Reed Biosystematics Unit, and as a Research Technician and Laboratory Manager in Adam Kepecs' systems neuroscience lab at Cold Spring Harbor Laboratory. She is interested in population genetic inference and spatial genetics, and was previously supported by the T32 in Biological Data Science.
Cole Williams graduated from UC Davis in 2019 where he worked in Dr. Brenna Henn’s group studying the use of identity-by-descent (IBD) to infer pedigree relationships. He spent a year at the Colorado Center for Personalized Medicine in the labs of Dr. Chris Gignoux and Dr. Ethan Lange before joining the Ramachandran lab and the Computational Biology PhD program in September 2020. At Brown's Computational Biology doctoral program, he wants to continue thinking about IBD but is also interested in demographic inference, particularly sex-biased migration events, and coalescent theory. He is funded under his own NSF Graduate Research Fellowship, and was previously supported by the T32 in Biological Data Science.
Hannah Snell graduated from Boston University in 2023 with a Master of Science in Biostatistics. During her time at BU, she worked at the Broad Institute of MIT and Harvard in the Genomic Center for Infectious Diseases where she studied population-level genomics and antifungal resistance in pathogenic fungi. Prior to her time at BU and the Broad Institute, Hannah received her dual bachelor's degrees in Biochemistry and Statistical and Data Sciences from Smith College. Hannah's primary research interests include building new tools to study evolutionary events such as selective sweeps. She is also interested in utilizing population genomics data to better understand drug discovery and pharmacological design, and is co-advised by Sohini and CCMB/CS colleague Ritambhara Singh.
Shevaughn Holness graduated from Smith College in 2023 with dual bachelor's degrees in Biological Sciences and Statistical & Data Sciences. During her time at Smith, she worked remotely with Dr. Andrew Dahl on a method that uses assortative mating to detect when polygenic scores start to be significantly influenced by population structure. Currently, Shevaughn is in Brown’s Computational Biology doctoral program where she is co-advised by Sohini Ramachandran and Lorin Crawford. She is studying incorporating environment information into polygenic risk score models as well as identity-by-descent sharing between geographic locations.
Abby Kuntzleman graduated from Duke University in 2021 with degrees in biology and computer science where she worked remotely with Dr. Tony Capra using ancient DNA to understand patterns of Neanderthal introgression over time. Before joining the lab as a postbaccalaureate researcher funded by Brown's NIH PREP Scholar program, Abby worked as a STEM elementary educator in Michigan inspiring a love for science and coding in the next generation. She also worked as a researcher with Dr. Stephen Techtmann using machine learning and microbial communities to predict oil contamination in lakes. Currently, Abby is developing methods for inferring aspects of archaic introgression histories from modern human genomes; she is also interested in the evolution of life history traits and sex-specific trait architecture. Abby is supported by the T32 in Biological Data Science and will also be supported by her own NSF Graduate Research Fellowship.
Postdoc (2013-2016) -> Assistant Professor at Stanford University, Statistics and Biomedical Data Science.
Postdoc (2014-2019) -> Assistant Professor of Statistics at Duquesne University, Mathematics and Computer Science.
Postdoc (2017-2020) -> Postdoctoral Fellow, Center for Genomic Health, Icahn School of Medicine at Mount Sinai -> Senior Scientist, Statistical Genetics, Bristol Myers Squibb.
Postdoc (2018-2019) -> tenure-track Assistant Professor of Archaeology at Wesleyan University. For more information, see Kate's professional website.
ICERM postdoc (2019-2020), coadvised by Ramachandran and Lorin Crawford -> Research Scientist in Health AI Division of AI/ML at Apple Inc. For more information, see Greg's professional website.
Postdoc (2022-2024) -> Postdoctoral Fellow, University of Copenhagen.
Grad student (2013-2018) -> Postdoc in the Hirschhorn Lab at Boston Children's Hospital, Broad Institute of MIT and Harvard, and Harvard Medical School (2019-2021) -> Senior Statistical Geneticist, Regeneron (2021).
Grad student (2014-2017), graduated from Fairbrother Lab at Brown University (2021) -> postdoc in in Steven Reilly’s lab at Yale University.
MD/PhD student (Grad student 2017-2021) -> completed MD in 2023 -> internal medicine resident at Massachusetts General Hospital/Harvard Medical School.
Grad student (2017-2022) -> postdoctoral researcher with Arbel Harpak at the University of Texas at Austin (2023-Fall 2024) -> Senior Scientist, Genomics PLC.
Grad student (2017-2022) -> Research Scientist at Meta.
Grad student (2017-2022) -> Computational Scientist at the Samocha Lab, Broad Institute of Harvard/MIT.
MS in Ecology, Evolution, and Organismal Biology at Brown University (2022-2024) -> Scientist II, Eurofins PSS Insourcing Solutions
Selena concentrated in Computational Biology (UTRA, 2020 and honors thesis in the Ramachandran Lab) -> coauthor on multiple papers from the lab -> matriculated at Harvard Medical School in Fall 2022.
researcher focused on improving SWIF(r) and analyses of UK Biobank research;dual concentrator in Computer Science and Business Economics -> software engineer, advertising division of Microsoft.
researcher focused on incorporating additional visualizations into pong (2017-2018); AB in Computer Science -> Software Engineer at Grail in Menlo Park, CA.
research assistant (2012-2014) -> Schurr lab at Penn (PhD student).
UTRA (2014) and thesis student (2015); AB in Computer Science and ScB in Biology (2015); Brown's Kidwell Prize in Genetics and Population Biology (2015) -> Oracle's Big Data Discovery team in Cambridge -> PhD student at Stanford University (2020-present).
funded through an REU supplement to NSF CAREER DBI-1452622 (summer 2015); Computer Science major and Italian Studies minor, Wellesley College '17.
coauthor on pong; funded by Brown University, CCMB, an UTRA (summer 2016), and an REU supplement to NSF CAREER DBI-1452622 (summer 2016); ScB in Computer Science and Religious Studies (2017) -> Microsoft, Seattle as a software engineer on the Shell Team of the Windows and Devices group (summer 2017 - present).
researcher during summer 2016-summer 2017, funded by a Summer Research Assistantship under Brown's Program in Liberal Medical Education and NSF CAREER DBI-1452622 -> Class of 2017 Senior Prize in Biology, ScB in Applied Mathematics-Biology -> Software Engineer at Yelp.com, on Yelp’s Messaging Intelligence team.
Capstone project (Fall 2016) -> technology group at Capital One, Washington DC.
high school researcher (2012-2014) -> Emory University '18.
UTRA (2012) and thesis student (2013); ScB in Biology (2013) -> Fulbright Scholar in Montpelier, France (2013-2014) -> Clinical Data Analyst with Foundation Medicine, Boston MA (09/2014-08/2017) -> PhD student, Genome Sciences, University of Washington, Seattle (09/2017 - present).
UTRA (2011) and thesis student (2012); AB in Classics and ScB in Biology (2012) -> Simons Fellow in Computational Neuroscience at the Marcus Autism Center in Atlanta, GA (2012-2014).
UTRA (2013) and thesis student (2015); ScB in Applied Mathematics - Biology (2015) -> New England Conservatory of Music (Masters in Violin performance received spring 2017) -> PhD student, Biostatistics, Harvard University (09/2017 - present).
UTRA (2013).
The Crawford Lab at Brown University is in the Department of Biostatistics, and develops methods to address problems in quantitative genetics and cancer biology. We collaborate on developing model-based approaches to detect genomic interactions underlying complex traits, at a variety of genomic scales.
The Henn Lab at the University of California, Davis investigates patterns of human genetic diversity and evolution by pairing genomic datasets with phenotypic, linguistic, and prehistoric information. We are collaborating on applications of our new selection detection methods to African population genomic data and functional genomic data.
The Huerta-Sanchez Lab at Brown University is in Ecology and Evolutionary Biology and the Center for Computational Molecular Biology, and focuses on theoretical population genetics and the integrated analysis of modern and ancient genomes. We collaborate on the development and application of new coalescent-based methods for inference of population histories using ancient and extant DNA.
The Raphael Lab at Princeton University focuses on cancer genomics. We are collaborating on methods for identifying pathways underlying common diseases using germline genetic data from cases and controls.
The Rosenberg lab at Stanford University has been a longtime collaborator with our group in studies of human population structure.
The Sandstede group at Brown University is in the Division of Applied Mathematics, and focuses on dynamical systems and the topology of high-dimensional datasets. We are collaborating on developing new methods to identify human phenotypes that share a core set of mutated genes in large genome-wide association datasets.
The Van Allen lab at the Dana-Farber Cancer Institute drives precision cancer medicine through clinical computational oncology. We collaborate on the integrated analysis of germline and somatic data to test for predisposition to immune infiltrate in cancers.
Dr. Yang is on the faculty at St. Jude Children's Research Hospital, in the Department of Pharmaceutical Sciences. Our collaboration centers around characterizing the evolutionary history of mutations associated with incidence and treatment outcome in acute lymphoblastic leukemia.
There are positions available for postdoctoral fellows to join the lab. Applicants must have a background in theoretical population genetics with experience analyzing data, or a quantitative background (e.g., statistics, computer science) with a desire to do research in genetics and evolutionary biology. Programming skills and proficiency in Unix-based computing environments are very desirable. Successful candidates may develop projects related to any of the lab’s research topics, or propose new projects related to the lab's interests.
Informal inquiries as well as applications (consisting of a CV, copies of relevant publications, and contact information for at least two references) should be emailed to Sohini Ramachandran or (in 2021) deposited via Interfolio.
The Ramachandran lab is recruiting prospective PhD students via the doctoral programs in Ecology and Evolutionary Biology, Computational Biology, and Molecular Biology, Cell Biology and Biochemistry. For information, see the doctoral program pages and email Sohini Ramachandran.
Undergraduates interested in pursuing research in the lab should ideally have taken at least one semester of programming (e.g. CS150 or CS170) at Brown; familiarity with Unix utilities, Perl, and/or R is a plus. Email Sohini Ramachandran about joining the lab, and also note that UTRA can fund undergraduate researchers for a semester or the summer.