Aidan Swope

Research Scientist at Harmonic

California, United States
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Summary

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Aidan Swope is a research scientist in California with 11 years of experience building neural networks that aim to reason more like humans. He focuses on scalable automated reasoning via language models, reinforcement learning, human preference modeling, and discrete search methods applied to theorem proving and circuit optimization. His selected work includes LeanDojo for retrieval-augmented theorem proving and CircuitVAE for latent circuit optimization, and he has probed why networks struggle with math through collaborations at Caltech and NVIDIA. At Harmonic he develops LM + search + RL systems to solve formal math problems at competition and research level, blending deep theory with practical engineering. Known for “making big, wiggly functions and taming them with optimization,” he brings both hands-on ML systems experience and a taste for elegant, principled solutions.
code11 years of coding experience
job5 years of employment as a software developer
bookCalifornia Institute of Technology
bookIolani School
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Stackoverflow

Stats
1reputation
0reached
1answer
0questions
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Github Skills (44)

lifecycle9
artificial-intelligence9
lightning9
representation-learning9
pytorch9
python9
machine-learning9
sensor9
sensor-fusion9
ai9
scheme9
deep-learning9
fusion9
code-completion8
infrastructure8

Programming languages (3)

C++RustPython

Github contributions (5)

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A large-scale LED installation in the alley of Hell.
Contributions:73 commits, 6 PRs, 80 pushes in 3 years 4 months
raspberry-piledinstallationscalelarge-scale
Open-source code for the paper "Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach ".
Contributions:64 commits, 1 comment in 1 month
representationsensor-fusionsensingdeep-learningapproach
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Aidan Swope - Research Scientist at Harmonic