Graduate School of Medical Sciences
A partnership with the Sloan Kettering Institute

2023 National Science Foundation’s Graduate Research Fellowship Program

Written by Nora McCall
Thursday, March 14, 2024

Weill Cornell Graduate School congratulates our current graduate students who received award offers and
honorable mention recognition in the 2023 National Science Foundation’s Graduate Research Fellowship
Program!


Tzippora Chwat-Edelstein | Honorable Mention | BCMB PhD Program | Mentored by Dr. Xiaolan Zhao at
Memorial Sloan Kettering Cancer Center | “SUMO E3 auto-sumoylation in substrate selection and pathway
regulation”


Victoria Jordan | Award Accepted | Tri-I Chemical Biology PhD Program | Mentored by Dr. Heeseon An at
Memorial Sloan Kettering Cancer Center | “Studying ubiquitin Ser57 phosphorylation as a regulator of neuronal
differentiation”


Benjamin Kaminow | Award Accepted | Tri-I Computational Biology & Medicine PhD Program | Mentored by
Dr. John Chodera at Memorial Sloan Kettering Cancer Center | “Development of structure-enabled machine
learning models for protein-ligand binding affinity prediction”


Divya Koyyalagunta | Award Accepted | Tri-I Computational Biology PhD Program | Mentored by Dr. Quaid
Morris at Memorial Sloan Kettering Cancer Center | “Interpretable Machine Learning for Simultaneous
Inference of Phylogeny and Migration”


Linda Ma | Award Accepted | Computational Biology MS Program | Mentored by Dr. Jonathan Victor at Weill
Cornell Medicine | “Representation of neural activity as walks in curved spaces”
Christopher Nieves-Escobar | Award Accepted | Tri-I Chemical Biology PhD Program | Mentored by Dr.
Derek Tan at Memorial Sloan Kettering Cancer Center | “Photoredox-catalyzed synthesis of 3-oxetanols and 3-
azetidinols from carboxylic acids”


Austin Varela | Award Accepted | Computational Biology MS Program, Incoming Tri-I Computational Biology
& Medicine PhD Program | Mentored by Dr. Ashley Laughney at Weill Cornell Medicine | “Using generative
modeling to understand heterogeneous patterns of cellular communication from scRNA-seq data"

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