Summary
Matthew Parno is a computational scientist and technology leader with a decade of experience at the intersection of energy systems, physics-based modeling, Bayesian statistics, and generative machine learning. As Co-Founder and CTO of Distill Energy and Principal Researcher at Solea Energy, he translates advanced research into practical tools that improve sustainability and system-level understanding. His career spans academia and national labs, including a PhD from MIT and research roles at Dartmouth and the Cold Regions Research and Engineering Laboratory, where he developed high-fidelity models and inverse problem solutions. Known for blending theoretical rigor with applied engineering, he has a track record of moving complex simulation and uncertainty-quantification methods toward real-world deployment. Based in rural Vermont, he brings both entrepreneurial grit and hands-on scientific programming experience—sometimes rooted in surprising places, from CUDA GPU work on radar inverse problems to river guiding—reflecting a pragmatic, interdisciplinary approach to problem solving.
10 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy (PhD), Computational Science and Engineering, 4.92, Doctor of Philosophy (PhD), Computational Science and Engineering, 4.92 at Massachusetts Institute of Technology
Bachelor of Science (BS), Electrical and Electronics Engineering, 3.88, Bachelor of Science (BS), Electrical and Electronics Engineering, 3.88 at Clarkson University
English