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Interpreting and Designing Deep Learning for Regulatory Genomics

Cold Spring Harbor Laboratory

The Koo Lab, based at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory, develops interpretable and generalizable deep learning approaches to decode the regulatory genome. Our research aims to uncover the sequence–function relationships that drive gene regulation, protein behavior, and cellular phenotypes. We focus on designing biologically informed predictive and generative models, building foundational AI systems for genomics and protein sequences, and developing interpretability tools that translate model outputs into mechanistic insights. A central goal of the lab is to move beyond black-box predictions and toward AI-driven discovery, particularly in the context of gene regulation, disease mechanisms, and cancer biology.

Main Areas of Interest

Predictive and Generative AI for Regulatory Genomics: Developing sequence-to-function and function-to-sequence models that capture cis-regulatory logic and enable rational design.
Model Interpretability: Building robust methods to extract biologically meaningful mechanisms from deep learning models and reveal the cis-regulatory code.
Active Learning for AI-Experiment Integration: Creating data-efficient strategies to guide functional experiments, especially in low-data regimes.
Agentic AI for Scientific Discovery: Exploring frameworks where AI agents can autonomously iterate between modeling, interpretation, and hypothesis refinement to accelerate biological discovery.

Specialties

Artificial Intelligence

Genetics & Genomics

Techniques You Will Learn

Deep Learning (CNNs, Transformers, Diffusion Models)
Functional Genomics (MPRA, ATAC-seq, RNA-seq, CRISPR screens)
Explainable AI
Active and Continual Learning
Agentic AI and AI-Orchestrated Experimental Design

Curriculum

The lab provides a strong interdisciplinary training environment with regular one-on-one mentoring, weekly group meetings, and participation in journal clubs and reading groups. Trainees are encouraged to pursue both technical and professional development, including opportunities for collaboration, presentations, and engagement with the broader scientific community at CSHL.

Accommodation

CSHL offers on-campus housing for visiting scientists and provides assistance with off-campus housing options for longer-term researchers.

Fees and Program Logistics

This program has the following durations available:

Duration	Fee
6 weeks	$2,250.00

This program allows Merit Applications. This program allows merit-based applications for virtual and onsite clinical and research programs. If you are successfully awarded under this category, Trialect or the host mentor will cover the tuition fee only. All applications will be evaluated based on merit. Due to the high level of competition, the chances of being selected under the merit category are quite limited.

Who Can Apply

A PhD in Computational Biology, Bioinformatics, Computer Science or related field is required. Strong computational biology and machine learning experience is required. Ideal candidates will be self-driven, have strong written and oral communication skills, and a desire to work in an interdisciplinary environment. Experience with active learning, continual learning, generative AI, bioinformatics, agentic AI, or explainable AI is a plus.

Host Details

Host Name: Koo Lab

Affiliation: Cold Spring Harbor Laboratory

Address: 1 Bungtown Road

Website URL: https://www.cshl.edu/

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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