Sarah Gurev

Sarah Gurev

PhD Student in Computer Science

MIT EECS

I am an MIT PhD student in Debora Marks lab (Harvard). I am interested in deep generative models of proteins, especially modeling viral and antibody sequences and structures. My research develops models to learn viral antibody escape by combining fitness models with biophysical and structural information. These models can be used to identify high escape variants and to design and test future-proof vaccines.

Interests
  • Machine learning for biology
  • Computational vaccine design
  • Viral evolution
  • Protein engineering
Education

  • MS in Electrical Engineering and Computer Science, 2023

    Massachusetts Institute of Technology

  • BS in Computer Science, 2020

    Stanford University

Publications

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Noor Youssef, Sarah Gurev, Fadi Ghantous, Kelly Brock, Javier Jaimes, Ann Dauphin, Amy Sherman, Leonid Yurkovetsky, Daria Soto, Ralph Estanboulieh, Ben Kotzen, Pascal Notin, Aaron Kollasch, Alexander Cohen, Sandra Dross, Jesse Erasmus, Deborah Fuller, Pamela Bjorkman, Jacob Lemieux, Jeremy Luban, Michael Seaman, Debora Marks (2024). Protein design for evaluating vaccines against future viral variation. BioRxiv.

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Nicole N Thadani, Sarah Gurev, Pascal Notin, Noor Youssef, Nathan J Rollins, Daniel Ritter, Chris Sander, Yarin Gal, Debora S Marks (2023). Learning from prepandemic data to forecast viral escape. Nature.

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Ada Shaw, Hansen B Spinner, June Shin, Sarah Gurev, Nathan Rollins, Debora S Marks (2023). Removing bias in sequence models of protein fitness. BioRxiv.

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