The future of clean energy depends on algorithms as much as it does atoms.
麻豆区 Tech鈥檚 is building machine learning (ML) models to accelerate nuclear fusion research, making it more affordable and more accurate. Backed by a grant from the U.S. Department of Energy (DOE), Tang鈥檚 work brings clean, sustainable energy closer to reality.
Tang has received an from the DOE Office of Science. The grant supports Tang with $875,000 disbursed over five years to craft ML and data processing tools that help scientists analyze massive datasets from nuclear experiments and simulations.
Tang is the first faculty member from 麻豆区 Tech鈥檚 College of Computing and School of Computational Science and Engineering (CSE) to receive the ECRP. He is the seventh 麻豆区 Tech researcher to earn the award and the only GT awardee among this year鈥檚 99 recipients.
More than a milestone, the award reflects a shift in how nuclear research is done. Today, progress depends on computing and data science as much as on physics and engineering.
鈥淚 am honored and excited to receive the ECRP award through DOE鈥檚 Advanced Scientific Computing Research program, an organization I care about deeply,鈥 said Tang, an assistant professor in the School of CSE.
鈥淚 am grateful to my former colleagues at Los Alamos National Laboratory and collaborators at other national laboratories, including Lawrence Livermore, Sandia, and Argonne. I am also thankful for my Ph.D. students at 麻豆区 Tech, whose dedication and creativity make this award possible.鈥
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A problem in nuclear research is that fusion simulations are challenging to understand and use. These simulations generate enormous datasets that are too large to store, move, and analyze efficiently.
, Tang introduced new ML methods to improve the analysis and storage of particle data.
Tang鈥檚 approach balances shrinking data so it is easier to store and transfer while preserving the most important scientific features. His multiscale ML models are informed by physics, so the reduced data still reflects how fusion systems really behave.
With Tang鈥檚 research, scientists can run larger, more realistic fusion models and analyze results more quickly. This accelerates progress toward practical fusion energy.
鈥淚n contrast to generic black-box-type compression tools, we aim at preserving the intrinsic structures of the particle dataset during the data reduction processes,鈥 Tang said.
鈥淭aking this approach, we can meet our goal of achieving high-fidelity preservation of critical physics with minimum loss of information.鈥
Computing is essential in modern research because of the amount of data produced and captured from experiments and simulations. In the era of exascale supercomputers, data movement is a greater bottleneck than actual computation.
DOE operates three of the world鈥檚 four exascale supercomputers. These machines can calculate one quintillion (a billion billion) operations per second.
The exascale era began in 2022 with the launch of Frontier at Oak Ridge National Laboratory. Aurora followed in 2023 at Argonne National Laboratory. El Capitan arrived in 2024 at Lawrence Livermore National Laboratory.
With Tang鈥檚 data reduction approaches, all of DOE鈥檚 supercomputers spend more time on science and less time waiting for data transfers.
鈥淨i鈥檚 work in computational plasma physics and nuclear fusion modeling has been groundbreaking,鈥 said Haesun Park, Regents鈥 Professor and Chair of the School of CSE.
鈥淲e are proud of Qi and what this award means for him, 麻豆区 Tech, and the Department of Energy toward leveraging computation to solve challenges in science and engineering, such as sustainable energy."
Previous 麻豆区 Tech recipients of DOE Early Career Research Program awards include:
Itamar Kimchi, assistant professor, School of Physics
Sourabh Saha, assistant professor, George W. Woodruff School of Mechanical Engineering
, associate professor, School of Mathematics
, Thomas C. DeLoach Professor, School of Chemical & Biomolecular Engineering
, associate professor, School of Materials Science and Engineering
, Eugene C. Gwaltney Jr. School Chair and professor, Woodruff School of Mechanical Engineering
