Concurrently, I am a visiting researcher in the Wayment-Steele Lab at University of Wisconsin-Madison. In addition to my PhD work, I serve as a two-time program organizer for the Machine Learning in Structural Biology Workshop at NeurIPS, guest editor of the Machine Learning in Structural Biology Special Collection in PRX Life, and reviewer for ICML and ICLR conferences.
I graduated with a BS in Computer Science, BA in Biophysics, and BS in Applied Math and Statistics from Johns Hopkins University. At Hopkins, I was an undergraduate researcher with Doug Barrick and previously the-late James Taylor. I also worked in the Integrated Imaging Center as a cryo-EM research assistant under the supervision of Michael McCaffery. I spent a lot of my time at Hopkins as a Teaching Assistant for Introductory Physics (AS.171.107/108), Biophysical Chemistry (AS.250.372), and Protein Engineering & Biochemistry Lab (AS.250.253).
This Spring 2025, I will be lead instructor for the mini-course at Stanford titled BIOS 429: Protein Design and Modeling using Machine Learning. In Fall 2023, I was a Graduate Teaching Asistant for BIOPHYS 242: Biological Macromolecules.
When I'm not doing research, you can probably find me weightlifting, hiking, eating, or with friends.
If you are interested in getting in touch or collaborating, please feel free to reach out!
Selected Publications
Learning millisecond protein dynamics from what is missing in NMR spectra
Hannah K. Wayment-Steele* Gina El Nesr*, Ramith Hettiarachchi, Hasindu Kariyawasam, Sergey Ovchinnikov, Dorothee Kern (* = equal contribution)
bioRxiv '25 | Biophysics
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Editorial: Machine Learning for Structural Biology
Gabriele Corso† Gina El Nesr†, Hannah K. Wayment-Steele† († = authors listed in alphabetical order)
PRX Life '24 | Special Collection MLSB
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An all-atom protein generative model
Alexander E. Chu, Jinho Kim, Lucy Cheng, Gina El Nesr, Richard W. Shuai, Minkai Xu, Po-Ssu Huang.
PNAS '24 | Proceedings of the National Academy of Sciences
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Singular value decomposition of protein sequences as a method to visualize sequence and residue space
Autum R. Baxter-Koenigs*, Gina El Nesr*, Doug Barrick.
(* = equal contribution)
PS '22 | Protein Science
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Recorded Talks
Machine Learning for Protein Engineering
Talked about SVD for visualizing sequence and residue space.
Virtual Talk
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Selected Awards
NSF Graduate Research Fellowship
2022 | Stanford University
Institute for Data Intensive Engineering and Science (IDIES) Student Summer Research Fellowship
2020 | Johns Hopkins University
Jason H.P. and Beverly Kravitt Fund Fellow
2019 | Johns Hopkins University
Woodrow Wilson Research Fellowship
2018 | Johns Hopkins University
Academic Service
Guest Editor
2024-Present | American Physical Society - PRX Life
Workshop Organizer + Reviewer
2023-Present | Machine Learning in Structural Biology (MLSB) @ NeurIPS
Workshop Reviewer
2024 | GEM.bio @ International Conference on Learning Representations (ICLR)
Workshop Proposal Committee
2024 | International Conference on Machine Learning (ICML)