Samuel Kim
Senior Research Scientist at The Johns Hopkins University Applied Physics Laboratory
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolSamuel Kim is a senior research scientist at Johns Hopkins APL with 13 years of experience bridging computational physics, photonics, and machine learning. He earned a PhD from MIT where he designed silicon-photonic lenses for solid-state LiDAR, developed deep learning architectures for symbolic regression of PDEs and image-driven systems, and created Bayesian optimization approaches for high-dimensional materials design. His work spans hands-on electromagnetic simulation and custom numerical code for thermal and additive-manufacturing modeling to leadership as co-founder/CSO of a photonics startup. At APL he applies multidisciplinary expertise to metamaterials, antennas, sensors, and quantum devices, often combining COMSOL/CST modeling with bespoke algorithms. Known for reducing system complexity—e.g., achieving 2x horizontal FOV with orders-of-magnitude simplification versus OPAs—he brings both theoretical rigor and practical engineering to high-impact applied research. Based in Cambridge, MA, he blends academic depth with entrepreneurial drive to accelerate complex, physics-informed technology development.
13 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Electrical Engineering and Computer Science (EECS), Master of Science - MS, Electrical Engineering and Computer Science (EECS) at Massachusetts Institute of TechnologyBachelor’s Degree, Physics, Bachelor’s Degree, Physics at Harvard University
Github Skills (39)
visualization9
gradient-descent9
julia9
symbolic-regression9
periodic8
data-visualizations8
computation7
gaussian-processes7
kernel7
gpu7
diffraction7
deep-learning7
bayesian-inference7
neural-network6
photonics6
Programming languages (5)
Github contributions (5)
samuelkim314/DeepBO
Jul 2022 - Sep 2022
Contributions:13 commits, 13 pushes, 1 branch in 2 months
samuelkim314/DeepSymReg
Oct 2020 - Mar 2022
Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
Contributions:5 commits, 3 PRs, 5 pushes in 1 year 5 months
deep-learningneural-networksymbolic-regressionequation-discovery
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