The Koo Lab studies the functional impact of genomic mutations through a computational lens using data-driven machine learning solutions. We are broadly interested in applications for studying gene regulation and protein (dys)function. Our approach develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy biological sequence data. Our goal is to elucidate biological mechanisms that underlie sequence-function relationships, with a broader aim of advancing precision medicine for complex diseases, including cancer. We are part of the Simons Center for Quantitative Biology and the Cancer Center at Cold Spring Harbor Laboratory.

Lab News

  • Apr 13, 2022 -- Lucia Tellez PĂ©rez, a CSHL grad student, starts rotation. Welcome Lucia!
  • Mar 11, 2022 -- Shush is awarded a NVIDIA Academic Hardware Grant!
  • Mar 7, 2022 -- Evan Seitz, from the Frank lab at Columbia, starts postdoc (jointly advised with Justin Kinney)!
  • Mar 1, 2022 -- Thomas Huffstutler, a masters student in Applied Math and Statistics at Stony Brook University, starts rotation.
  • Jan 22, 2022 -- Rohan Ghotra is a 2022 Regeneron STS Finalist!