/DOE Computational Science Graduate Fellowship Chooses Record 32 Fellows for 2021-22 (via Qpute.com)
DOE Computational Science Graduate Fellowship Chooses Record 32 Fellows for 2021-22

DOE Computational Science Graduate Fellowship Chooses Record 32 Fellows for 2021-22 (via Qpute.com)

May 11, 2021 — A record-setting class of 32 fellows will join the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) this fall as they train to apply high-performance computing to research across a range of fields, from atmospheric science to condensed matter physics and quantum information.

The program, established in 1991, trains top leaders in computational science. With the 2021-22 class, more than 550 students will have entered the fellowship. More than 400 now work in fields that support computing’s capacity to address problems important to the nation’s future.

Here are the newest fellows, their institutions and subject areas:

Jezrielle Annis
Texas A&M University
Physical Chemistry

Olivia Asher
University of Georgia

Lucas Attia
Massachusetts Institute of Technology
Chemical Engineering

Alexandra Ballow
Montana State University
Algebra and Quantum Mechanics

Zoe barbeau
Stanford University

Bryn Barker
University of North Carolina at Chapel Hill
Applied Mathematics

Paul Beckman
New York University

Vivek Bharadwaj
University of California, Berkeley
HPC/Scientific Computing

Marianne Cowherd
University of California, Berkeley
Ecosystem Sciences

Ishani Ganguly
Columbia University
Theoretical Neuroscience

Krystian Ganko
Massachusetts Institute of Technology
Chemical Engineering

Souradip Ghosh
Carnegie Mellon University
Computer Science

Juan (Felipe) Gomez
Harvard University
Condensed Matter Theory

Jalen Harris
Cornell University
Materials Science and Engineering

Bowen Jing
Massachusetts Institute of Technology
Computer Science

Gabrielle Jones
University of Michigan

Caleb Ju
Georgia Institute of Technology
Computational Science and Engineering

Olorundamilola (Dami) Kazeem
Johns Hopkins University
Computational Linguistics

Madeleine Kerr
University of California, San Diego

Joy Kitson
University of Maryland, College Park
Computer Science

Nicole Pagane
Massachusetts Institute of Technology
Computational and Systems Biology

Shehan Parmar
University of California, Los Angeles
Aerospace Engineering

Abigail Poteshman
University of Chicago
Computational and Applied Mathematics

Sonia Reilly
New York University

Paulina rodriguez
The George Washington University
Mechanical Engineering

Rahul Sahay
Harvard University

Courtney Shafer
University at Buffalo
Geological Sciences

Timothy (Joey) Taylor
University of Colorado at Boulder
Atmospheric Science

Samuel Varner
California Institute of Technology
Chemical Engineering

Julia Wei
University of California, Berkeley
Condensed Matter Physics, Quantum Information

Steven Wilson
Arizona State University
Chemical Engineering

Victor Zendejas Lopez
California Institute of Technology
Mechanical Engineering

The DOE CSGF includes a track for those pursuing an advanced degree in applied mathematics, statistics or computer science with research interests that help use emerging high-performance systems more effectively. Students focused on issues in high-performance computing as a broad enabling technology and not on a particular science or engineering application are included.

As part of the program, fellows receive exceptional benefits including a yearly stipend; full payment of university tuition and required fees (during the appointment period); and an annual academic allowance. Renewable for up to four years, the fellowship is guided by a comprehensive program of study that requires focused coursework in the areas of science and engineering, computer science and applied mathematics. It also includes a three-month practicum at one of 21 Department of Energy laboratories or sites across the country.

Additional details for each fellow will be available via the program’s online fellow directory in September. Meanwhile, please contact us for further information.

Source: DOE CSGF

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