Joel Harley
Associate Professor Of Electrical And Computer Engineering
Gainesville, Florida, United States
Summary
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Senior🎓
Top SchoolJoel Harley is an associate professor of electrical and computer engineering with 11 years of academic experience applying physics-based models to practical signal processing problems. His research blends low-cost, low-power sensing with advanced methods—compressive sensing, time reversal, matched field processing, and machine learning—to detect and localize damage in metal and concrete infrastructure. He has translated these techniques across domains including medical ultrasound, seismology, and underwater acoustics, seeking cross-disciplinary impact. Trained at Carnegie Mellon (PhD, 3.97 GPA) and a former researcher at MIT Lincoln Laboratory and Raytheon, he combines rigorous theoretical work with hands-on sensor and algorithm development. Notably, his work emphasizes inexpensive, deployable cyber-physical solutions for structural health monitoring rather than purely lab-scale demonstrations. Based in Gainesville, Florida, he focuses on integrating AI with physics to make structural evaluation scalable and field-ready.
12 years of coding experience
9 years of employment as a software developer
Bachelor of Science (BS), Electrical Engineering, GPA: 3.94/4.00, Bachelor of Science (BS), Electrical Engineering, GPA: 3.94/4.00 at Tufts UniversityDoctor of Philosophy (PhD), Electrical and Computer Engineering, GPA: 3.97/4.00, Doctor of Philosophy (PhD), Electrical and Computer Engineering, GPA: 3.97/4.00 at Carnegie Mellon University
Github Skills (5)
transfer-learning7
reinforcement-learning5
interpretable-machine-learning5
artificial-intelligence4
neural-network4
Programming languages (2)
Github contributions (3)
jbharley/WISP
Jul 2014 - Jan 2015
Contributions:6 commits, 1 push in 6 months
EAGG-UF/PRIMME
Mar 2022 - Mar 2022
The repository for the Physics-Regulated Interpretable Machine Learning Microstructure Evolution (PRIMME) framework for learning and emulating microstructure grain growth.
Contributions:5 commits, 2 PRs, 7 pushes in 1 day
interpretable-machine-learningmaterials-scienceartificial-intelligenceneural-networkreinforcement-learning
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