Koo Lab Group Photo 2025

The Koo Lab at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory develops machine learning methods to study gene regulation and biological sequence function, with an emphasis on interpretability, generalization, and mechanistic insight. Our work focuses on building and evaluating deep learning models that link DNA and protein sequence to molecular and cellular phenotypes, developing principled methods to interpret what these models learn, and integrating modeling with experimental perturbation data. We are particularly interested in understanding how regulatory logic is encoded in sequence, how model predictions vary across genetic and cellular contexts, and how AI-based models can be audited and refined to support biological discovery. Through these efforts, our goal is to move beyond black-box prediction toward computational frameworks that yield testable hypotheses and mechanistic understanding in regulatory genomics and cancer biology.

Recent News

  • Jan 16 Jan 16, 2025 -- Jakub's paper "Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models" is published in npj Digital Medicine!
  • Jan 07 Jan 7, 2025 -- Sophia Chen and Arnav Pemmaraju are Regeneron STS Scholars! Congrats!
  • Jan 07 Jan 7, 2025 -- Jessica's paper on "Uncertainty-aware genomic deep learning with knowledge distillation" is published in npj Artificial Intelligence!
  • Dec 25 Dec 25, 2025 -- NTv3, co-led by Amber (Instadeep) with contributions by Chandana and Evan, is on bioRxiv -- great collab with InstaDeep, Koo Lab, Stark Lab, and Kuleshov Lab!
  • Dec 07 Dec 7, 2025 -- Chandana and Amber present LLMGEN at NeurIPS Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences!
  • Nov 07 Nov 7, 2025 -- Yijie and Evan give oral presentations at Genome informatics meeting!
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