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

  • Apr 12 Apr 12, 2026 -- Koo lab alums, Steven Yu and Tianhao Luo, are selected for NSF Graduate Research Fellowship! Congrats!
  • Apr 08 Apr 8, 2026 -- Alan's and Moon's Research Highlight on "Predicting non-coding variant effects with AlphaGenome" is published in Cell Research!
  • Apr 07 Apr 7, 2026 -- Brian's manuscript on "Genetic background shapes AI-predicted variant effects" is on bioRxiv! Congrats!
  • Feb 01 Feb 1, 2026 -- Koo Lab Review/Perspective by Moon, Alan and Kaeli is on "Toward Interpretable and Generalizable AI in Regulatory Genomics" in on arxiv!
  • Jan 28 Jan 28, 2026 -- Peter has interviews with NY Times, Bloomberg, and Science News on DeepMind's AlphaGenome!
  • Jan 16 Jan 16, 2026 -- Jakub's paper "Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models" is published in npj Digital Medicine!
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