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NSF Sponsored REU in Bioinformatics and Computational Biology

CSHL's REU program in Bioinformatics and Computational Biology was supported by the NSF 2005 - 2014.

The technological advances in this century open a new realm of biological questions that can be addressed experimentally. Large genomic sequence or image datasets are routinely and quickly acquired, but the resources and expertise to analyze this data present a challenge to researchers. CSHL’s unique NSF REU program in Bioinformatics and Computational Biology addresses this need by providing early training to undergraduate students who might not otherwise pursue quantitative approaches. CSHL’s URP/REU students learn theory and techniques from an applied perspective, investigating an important biological problem rather than from the abstract perspective of computer science.  Students are mentored by expert CSHL researchers, who combine biology, information theory and sophisticated computational techniques to address questions at the frontiers of modern genomics, bioinformatics, and neuroscience.  In the past ten years, CSHL’s URP/REU program has recruited and trained a diverse group of students, many of whom are still working in bioinformatics or computational fields.  Almost all URP/REU participants have continued in scientific careers and/or advanced degree programs at competitive institutions. The program provides students with a modern quantitative biology training program that aims to inspire young scientists to become active participants in modern biological research with its demands for quantitative and computational skills.

Prospective REU Project Mentors

Dinu F. Albeanu - Neuronal circuits; sensory coding and synaptic plasticity; neuronal correlates of behavior; olfactory processing

Gurinder Atwal - Population genetics; bioinformatics; cancer; stochastic processes; statistical mechanics and information theory

Anne Churchland - Decision-making; electrophysiology; sensory processing; vision; audition; neural computation; modeling; behavior

Josh Dubnau - Learning; memory; genetics; behavior

Hiro Furukawa - Membrane proteins; x-ray crystallography; electrophysiology

Thomas Gingeras - Genome-wide organization of transcription and the functional roles of non-protein coding RNAs

Molly Hammell - Gene regulatory networks; integrated genomic analysis; bioinformatics; RNA biology; small RNAs

Ivan Iossifov - Computational biology; molecular networks; human genetics; human disease; applied statistical and machine learning; biomedical text-mining; molecular evolution

David Jackson - Plant development; stem cell signaling; genomics and imaging

Leemor Joshua-Tor - Structural biology; nucleic acid regulation; RNAi

Adam Kepecs - Decision-making; theoretical neuroscience; neuroeconomics

Justin Kinney - Sequence-function relationships; biophysics; deep sequencing; machine learning; transcriptional regulation; DNA replication

Alexei Koulakov - Theoretical neurobiology; quantitative principles of cortical design; computer science; applied mathematics

Alexander Krasnitz - Genomics of cancer; machine learning for biology; inference from noisy biological data; large-scale numerical computing.

Zachary Lippman - Plant development, genetics; molecular mechanisms of phase transitions for flowering time and inflorescence branching; heterosis

Rob Martienssen - Epigenetics; DNA methylation; chromatin and chromosome biology; transposable elements; RNA interference; stem cells; germline specification; plant genomics; plant evolution; aquatic plants

W. Richard McCombie - Genomics of psychiatric disorders; genomics of cancer; computational genomics; plant genomics

Partha P. Mitra - Neuroinformatics; theoretical engineering; animal communications; neural prostheses; brain mapping; developmental linguistics

Pavel Osten - Anatomical mapping of brain connectivity; neurological diseases

Darryl Pappin - Proteomics; mass spectrometry; protein chemistry

Michael Schatz - Genomics; genome assembly & validation; sequence alignment; high performance and multicore computing; parallel algorithms; cloud computing

Adam Siepel - Biological statistics; population genomics; evolution; transcriptional regulation

David L. Spector - Cell biology; gene expression; nuclear structure; microscopy

Marja Timmermans - Small RNA regulation; pattern formation; stem cell function

Glenn Turner - Neural coding; learning and memory; Drosophila

Doreen Ware - Computational biology; comparative genomics; genome evolution; diversity; gene regulation; plant biology

Michael Wigler - Human genetic disorders; population genetics; cancer genomics

Anthony Zador - Cortical mechanisms of auditory attention; neural computation; connectomics


Eligibility    All URP participants may take part in the Bioinformatics and Computational Biology program.  NSF-supported REU participants are selected from among the URP participants. Students supported by NSF must be citizens or permanent residents of the United States or its possessions.  If you are interested in bioinformatics and computational neuroscience, including research in any of the labs listed above, but are not a US citizen or permanent resident, you are eligibile for the program through sponsorship from non-restricted URP fellowships.
As for all URP participants, NSF-supported students must be currently enrolled as undergraduates.  An undergraduate student is a student who is enrolled in a degree program (part-time or full-time) leading to a baccalaureate or associate degree. Students who will have graduated before the program starts in July are not eligible.  Participants must be "returning to an undergraduate program" after the summer REU program. (click here for NSF eligibility requirements).

Additional Application Guidelines 

Overview of the summer curriculum

REU Program Alumni