Nathan Inkawhich
Research Engineer at Air Force Research Laboratory
United States
Summary
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Rockstar🎓
Top SchoolNathan Inkawhich is a research engineer with eight years of experience focused on adversarial machine learning, deep learning robustness and interpretability, and automatic target recognition for remote sensing. He holds a PhD in Computer Engineering from Duke and has a strong track record at the Air Force Research Laboratory developing robust ATR models, adversarial attacks, and computation-reducing approaches for SAR systems. Nathan has contributed official PyTorch tutorials (including DCGAN and transfer learning) during a stint at Facebook and as a PyTorch-focused ML engineer, signaling both practical model-building skills and a commitment to developer education. His background spans academia, government research, and industry internships where he applied DL-based object detection to complex satellite imagery and designed blackbox attacks at scale. Known for blending rigorous research with hands-on engineering—including VHDL DSP design for radar chains—he brings a rare mix of theoretical depth and low-level systems expertise.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Engineering, 4.00 / 4.00, Doctor of Philosophy - PhD, Computer Engineering, 4.00 / 4.00 at Duke UniversityBachelor of Science - BS, Computer Engineering, 3.98 / 4.00, Bachelor of Science - BS, Computer Engineering, 3.98 / 4.00 at Clarkson University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
computer-vision10
pytorch10
machine-learning10
deep-learning10
generative-adversarial-network10
transfer-learning9
develop9
Programming languages (3)
Github contributions (5)
pytorch/tutorials
Jul 2018 - Aug 2018
PyTorch tutorials.
Role in this project:
ML Engineer
ML EngineerContributions:16 commits, 3 PRs, 3 comments in 1 month
Contributions summary:Nathan's commits primarily involve working with PyTorch tutorials, indicating a focus on machine learning model development. Their contributions include initial commits for new tutorials related to DCGAN for generating faces, and finetuning torchvision models. Furthermore, the user made modifications to existing tutorials, refining code and addressing documentation, demonstrating a commitment to improving the learning materials for PyTorch users.
deep-learningpytorchpytorch-tutorials
inkawhich/machine-learning-course
Jan 2018 - Apr 2018
Contributions:18 commits, 15 pushes, 2 branches in 2 months
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