Adapted from UBNow
Published August 11, 2023
Two doctoral students in the School of Engineering and Applied Sciences are among 33 SUNY students selected as recipients of the 2023 SUNY Graduate Research Empowering and Accelerating Talent (GREAT) Awards.
The award recognizes outstanding students whose innovative research tackles some of society’s most pressing issues. Each recipient receives $5,000 in flexible funding for research expenses, professional development and stipend supplements.
The SEAS recipients are Grant Hecht, a student in the Department of Mechanical and Aerospace Engineering, and Isys Johnson, a student in the Department of Computer Science and Engineering.
The SUNY GREAT awards program, now entering its third year, provides incentives for SUNY graduate students to compete for federal awards sponsored by agencies, including the National Science Foundation, National Institutes of Health and U.S. Department of Energy, among others.
“Each of our GREAT Award recipients exemplify how SUNY is addressing complex problems with groundbreaking ideas and research,” said SUNY Chancellor John B. King Jr. “We are pleased to congratulate all 33 awardees for their dedication to improving the lives of others. Research will always be a pillar of higher education, especially at SUNY, and I urge all our students to always stay curious and seek out new ways to advance our society.”
Graham Hamill, vice provost for academic affairs and dean of the Graduate School, said UB’s doctoral students “work at the cutting edge of research, and their work demonstrates the ability of research to address society’s most urgent problems.
“Congratulations to all the award winners. At UB we are so pleased that our doctoral students are being recognized for their outstanding and impactful research,” he said.
For their award-winning projects, Hecht will investigate the computation of optimal spacecraft trajectories in cislunar and interplanetary space, helping facilitate the next generation of space missions by reducing mission costs while increasing the derived scientific knowledge. Johnson will apply structured linear algebra to machine learning methods to increase transparency in machine learning models.
Read the original story in UBNow.
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