DOE CSGF Welcomes 2023-2024 Class
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) will receive 39 new students for 2023-24, a new class size record. The incoming fellows will attend 25 universities – five of which are new to the program – across the country as they learn to apply high-performance computing (HPC) to research across a variety of fields, including computer science, astrophysics, electrical engineering, applied mathematics, and environmental fluid mechanics.
The program, established in 1991 and jointly funded by the DOE Office of Science and National Nuclear Security Administration (NNSA), trains top leaders in computational science. More than a third of the new fellows self-identify as women while more than half come from underrepresented groups. The 2023 cohort also includes a U.S. veteran and an active-duty service member.
With the addition of the 2023-24 class, the DOE CSGF will have onboarded more than 630 students across 33 cohorts. More than 475 program alumni work in an expanding number of fields that support computing's capacity to address problems important to the nation’s future.
The newest fellows, their institutions and subject areas as of the release date (subject to change) are:
- Zachary Andalman − Princeton University (Astrophysical Sciences)
- Alexandra Bardon − Massachusetts Institute of Technology (Computational Neuroscience)
- Luke Bhan − University of California, San Diego (Intelligent Systems, Robotics and Control)
- Amanda Bowden − University of Colorado Boulder (Atmospheric Science)
- Amelia Chambliss − Columbia University (Plasma Physics)
- Lauren Chua − Massachusetts Institute of Technology (Polymers and Soft Matter)
- Cameron Coles − Cornell University (Ecology and Evolutionary Biology)
- Luis Delgado Granados − University of Chicago (Theoretical and Computational Chemistry)
- Amil Dravid − University of California, Berkeley (Computer Science)
- Mohit Dubey − University of California, Berkeley (Environmental Engineering)
- Isabella Dula Razzolini − Stanford University (Atmospheric Science)
- Michelle Garcia − Dartmouth College (Theoretical/Computational Chemistry)
- Jacob Halpern − Columbia University (Plasma Physics)
- Emma Hart − Emory University (Computational Mathematics)
- Juampablo Heras Rivera − University of Washington (Mechanical Engineering)
- Iran Hernandez Imbert − Duke University (Mechanical Engineering)
- Elyssa Hofgard − Massachusetts Institute of Technology (Electrical Engineering and Computer Science)
- Benjamin Holmgren − Duke University (Computer Science)
- Abigail Keller − University of California, Berkeley (Ecology)
- Patrick Kim − Princeton University (Plasma Physics)
- David Krasowska − Northwestern University (Computer Science)
- James Larsen − University of Michigan (Applied and Interdisciplinary Mathematics)
- Adam Lechowicz − University of Massachusetts Amherst (Computer Science)
- Brandon Lee − Princeton University (Plasma Physics)
- Fiona Majeau − University of Colorado Boulder (Electrical Engineering)
- Ethan Meitz − Carnegie Mellon University (Molecular Simulation and Heat Transfer)
- Joshua Melendez-Rivera − Texas A&M University (Computational and Surface Chemistry)
- Marlo Morales − Washington State University (Physics)
- Benjamin Moyer − University of Maryland, College Park (Computational Geophysics)
- Kirill Nagaitsev − Northwestern University (Computer Science)
- Jennifer Paige − University of California, Davis (Applied Mathematics)
- Melissa Rasmussen − Stony Brook University (Astronomy)
- Pavan Ravindra − Columbia University (Chemical Physics)
- Gabriel Rios − Princeton University (Atmospheric Science)
- Kathlynn Simotas − University of California, Santa Barbara (Astrophysics)
- Joseph Torsiello − Temple University (Nuclear and Particle Physics - Theory)
- Cristian Villatoro − University of Notre Dame (Applied and Computational Mathematics and Statistics)
- Michael Walker − Princeton University (Fluid Mechanics)
- Sienna White − University of California, Berkeley (Environmental Fluid Mechanics)
“The CSGF is one-of-a-kind program that plays a critical role in training the highly skilled computational scientists, mathematicians and computer scientists that become tomorrow’s leaders for DOE and for the Nation,” said Dr. Ceren Susut-Bennett, DOE Acting Associate Director for Advanced Scientific Computing Research.
“This investment in training people who will lead the application of high-performance computing to solve problems that will expand our understanding of key scientific issues in research areas fundamental to support the future nuclear deterrent will create a direct pipeline of highly trained scientists and engineers into our workforce,” added Dr. Steve Binkley, Assistant Deputy Administrator for Research, Development, Test, and Evaluation in NNSA’s Office of Defense Programs.
The DOE CSGF’s interdisciplinary science and engineering track supports students in a range of fields, but all share a common element: applying HPC to research problems. A second track supports those studying applied mathematics, statistics, computer science, computer engineering or computational science – in one of those departments or their academic equivalent − with research interests that help scientists use emerging high-performance systems more effectively. This includes students focused on issues in HPC as a broad enabling technology rather than a particular science or engineering application.
Fellows receive exceptional benefits including a yearly stipend; full payment of university tuition and required fees; 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 DOE 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 or find the fellowship on Facebookand Twitter.