Summary
Akash Harapanahalli is a PhD candidate and Graduate Research Assistant blending rigorous mathematics, control theory, and machine learning to make AI-enabled control systems verifiably safe. He develops scalable computational tools—most notably the JAX-based immrax toolbox—for interval reachability and invariant set analysis, and has applied these methods to train neural controllers with formal safety guarantees. His work spans advanced topics like Lie-group reachable set computation and contracting dynamics on homogeneous manifolds, grounded in hands-on robotics experience from award-winning dual-arm manipulation projects. With nine years of multidisciplinary experience across academia and industry, he mentors students and seeks internships to translate theoretical advances into real-world systems. Notably, he pairs deep theoretical contributions with practical, GPU-scalable software—bridging the gap between provable guarantees and deployable controllers.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Georgia Institute of Technology
Dougherty Valley High School