Wilson earned his PhD in Applied Mathematics and Statistics from Johns Hopkins University in 2026, where his research focused on equivariant machine learning with applications to fluid dynamics. He earned his bachelor’s degrees in Computer Science and Mathematics from Rensselaer Polytechnic Institute and has industry experience in software engineering and machine learning. Mentored by IICD member Dr. Bianca Dumitrascu, his current research applies machine learning methods that leverage geometric structure and symmetries in data to biological problems, particularly the mechanical interactions of cells during embryonic development.
