Moe Khalil
Principal Member Of Technical Staff at Faculty of Engineering and Design - Carleton University
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolMoe Khalil is a Principal Member of Technical Staff at Sandia National Laboratories with nine years of experience advancing probabilistic and Bayesian methods to make ML and physics-based models robust under sparse, noisy data. He leads projects on probabilistic transfer learning and model calibration, applying his expertise to fluid-structure interaction, material modeling, and near-shore wave forecasting for energy harvesting. With a PhD in Civil Engineering and a background spanning electrical engineering and microbiology, he brings interdisciplinary rigor to computational mechanics, uncertainty quantification, and deep learning. As an adjunct research professor, he mentors graduate students and translates academic advances into practical, large-scale solutions on high-performance computing platforms. Notably, his work emphasizes statistical innovation—improving model calibration and robustness rather than just accuracy—to enable reliable decision-making in data-limited scientific applications.
9 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Civil Engineering, Doctor of Philosophy (Ph.D.), Civil Engineering at Carleton UniversityBachelor of Engineering (BEng), Electrical and Electronics Engineering, Bachelor of Engineering (BEng), Electrical and Electronics Engineering at McGill University
Github Skills (4)
uncertainty10
bayesian-inference9
uncertainty-quantification9
mcmc9
Programming languages (2)
Github contributions (2)
snl-dakota/dakota
Oct 2016 - May 2021
The core Dakota project
Contributions:149 commits in 4 years 7 months
sandialabs/UQTk
Aug 2019 - Mar 2020
Sandia Uncertainty Quantification Toolkit
Contributions:28 commits in 7 months
uncertainty-quantificationsnl-data-analysisbayesian-inferencesnl-science-libsuq
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