I’m Evan Seitz, a Computational Postdoctoral Fellow at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. My work focuses on interpreting deep neural networks trained on genomic data, with an emphasis on uncovering the regulatory mechanisms encoded in DNA sequence.

I’m the developer of SQUID and SEAM, two explainable AI methods for understanding what deep neural networks (DNNs) are learning from functional genomics data. SQUID interprets cis-regulatory mechanisms using surrogate models and was published in Nature Machine Intelligence. SEAM explores how genetic variation reshapes those mechanisms. I recently presented SEAM at the ICLR Generative and Experimental Perspectives for Biomolecular Design (GEM) Workshop with a preprint currently in preparation for journal submission.

Previously, I completed my PhD at Columbia University with Nobel laureate Joachim Frank, where I developed geometric machine learning and explainability frameworks to reveal conformational heterogeneity in cryo-EM protein structures. Across both molecular and genomic systems, a central theme in my work is using machine learning — and rigorous interpretation — to make sense of biological complexity.

Before my scientific career, I worked professionally in animation and communication, including a collaboration with Dr. Jennifer Aaker to direct and animate a series of educational videos based on research conducted at Stanford University, which premiered at the Future of StoryTelling Summit in NYC. I continue to enjoy visual storytelling—especially when designing figures and communicating results. Outside of science, I enjoy hiking, biking, tennis, and board games with friends and family.

This site serves as a hub for my work, ideas, and ongoing projects in computational biology and machine learning. You can download my:

• 📄 Academic CV
• 💼 Industry CV

Or connect with me on LinkedIn.