Bryson Gullett is a graduate research assistant and computer science master's student at the University of Tennessee who builds embedded software for neuromorphic computing and leads development of a Neuromorphic Computing Starter Kit that runs spiking neural networks on microcontrollers. He brings roughly a decade of hands-on experience across research and industry, having optimized neural networks for lower power and led a small team to make emerging brain-inspired hardware approachable for real-time applications. His background blends embedded C/C++, Unix workflows, and machine learning with practical web and API development from internships at Oak Ridge National Laboratory. Known for translating research into usable toolkits, he pairs rigorous academic performance with a pragmatic focus on deployable systems. Colleagues rely on him for team leadership on cross-disciplinary projects that bridge hardware constraints and ML models.
10 years of coding experience
2 years of employment as a software developer
High School Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES, 4.56/4.00 (weighted), 4.00/4.00 (unweighted), High School Diploma, HIGH SCHOOL/SECONDARY DIPLOMAS AND CERTIFICATES, 4.56/4.00 (weighted), 4.00/4.00 (unweighted) at Hardin Valley Academy
Bachelor of Science - BS, Computer Engineering, 3.99/4.00, Bachelor of Science - BS, Computer Engineering, 3.99/4.00 at University of Tennessee, Knoxville
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