Daniel Wang
Incoming Algorithms Development Engineer at Johns Hopkins Applied Physics Laboratory
United States
Summary
👤
Senior🎓
Top SchoolDaniel Wang is an algorithms-focused engineer with nine years of hands-on experience applying computational physics, signal processing, and machine learning to real-world defense and scientific problems. Currently joining Johns Hopkins APL as an Algorithms Development Engineer and researching stellarator magnetic-field modeling at UT Austin, he blends simulation-driven physics intuition with practical software engineering on HPC systems. His background includes building a transformer-based multi-object tracking pipeline at MIT Lincoln Laboratory, predictive ambient-noise models validated with field data in Arctic environments, and hardware-oriented electromagnetics work constructing a linear induction motor. Comfortable spanning Matlab, Python, and high-performance compute workflows, he seeks roles at the intersection of physical science and code—especially in aerospace and defense—where modeling fidelity and algorithmic rigor matter. An under-the-radar strength is his ability to move seamlessly between field-validated experimental data and production-ready simulation frameworks.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Computational Physics, Bachelor of Science - BS, Computational Physics at The University of Texas at Austin
Github Skills (15)
in-progress10
natural-language-processing9
language-processing9
datasets9
machine-learning9
named-entity-recognition9
nlp9
machine-translation9
bert7
mle1
generative1
pytorch1
deep-learning1
adversarial-learning1
draw1
Programming languages (1)
Github contributions (5)
dilinwang820/adaptive-f-divergence
Oct 2018 - Jan 2019
Contributions:25 commits, 21 pushes in 2 months
dilinwang820/matrix_svgd
Oct 2018 - Dec 2019
Contributions:14 commits, 2 pushes in 1 year 1 month
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