Biography

My name is Evan Seitz and I’m currently a Computational Postdoctoral Fellow in the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. Previously, I obtained my doctorate at Columbia University working on Computational Biophysics in the Frank Lab. I am interested in studying complex biophysical systems through a mathematically-rigorous and creative lens.

My thesis work is based on a geometric machine-learning approach to study hundreds of thousands of unorganized, extremely noisy single-particle cryo-EM images of an ensemble of biomolecules. Specifically, I used manifold embedding to elucidate metabolic function in the form of a low-dimensional energy landscape and corresponding continuum of atomic structures. Currently, my goal is to advance deep learning in functional genomics. Specifically, I’m aiming to bridge the divide between “black-box” deep neural network models in genomics and mechanistically interpretable biophysical models of gene regulation.

Almost a decade ago, previous to my scientific endeavors, my profession was in the arts (specifically 2D and 3D animation and design). Although my path has changed, I still love to design and find ways to incorporate those skills into my scientific work. As for me personally, I enjoy learning new things and sharing my ideas and experiences with others. In my free time, I have fun hiking, bicycling, playing sports like tennis, and team-based computer games.

This website provides an overview of my work in science and education. For more information, download my curriculum vitae

Interests
  • Biophysics
  • Complex Systems
  • Machine Learning
  • Algorithm Design
Education
  • PhD in Computational Biophysics, 2022

    Columbia University

  • MPhil in Biological Sciences, 2020

    Columbia University

  • MA in Biological Sciences, 2019

    Columbia University

  • BS in Physics, 2017

    Georgia Institute of Technology

  • BA in Mass Communication, 2009

    Georgia College

Projects

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Publications

A glycan gate controls opening of the SARS-CoV-2 spike protein
Nature Chemistry, 2021