Brighton Ancelin is a Machine Learning PhD student and Graduate Research Assistant at Georgia Tech with 11 years of engineering experience focused on efficient algorithms for non-convex manifold-constrained optimization, decentralized methods, and numerical linear algebra. His research spans subspace tracking, covariance estimation, decentralized SLAM, feature-distributed optimization, and reinforcement learning under Prof. Justin Romberg, while industry internships at NVIDIA, MathWorks, and Viasat grounded his work in compiler toolchains, CUDA linear-algebra optimizations, and automation for large-scale systems. He has repeatedly bridged theory and practice—building a Python API for TensorRT/Myelin, improving cuDNN convolution behavior, and accelerating antenna test workflows from days to minutes. A proven educator, he’s taught graduate courses in statistical estimation and matrix methods, translating complex material into assignments and exams. Academically he holds a perfect GPA across BS, MS, and current PhD studies at Georgia Tech and has spent time at EPFL, signaling both breadth and deep technical rigor.
11 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, 4.0 GPA, Doctor of Philosophy - PhD, Machine Learning, 4.0 GPA at Georgia Institute of Technology
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