Principal Investigator: Matthew Willsey attended MIT where he received B.S. and M.Eng. degrees in Electrical Engineering with a research focus in digital signal processing. He later attended medical school at Baylor College of Medicine and completed his neurosurgery residency at the University of Michigan  in 2022. During his residency, he completed an enfolded CAST-approved fellowship in Stereotactic and Functional Neurosurgery and a PhD in Biomedical Engineering during his resident research time plus an additional leave-of-absence year. After graduation, he completed a one-year, post-graduate appointment as a clinical instructor stereotactic/functional neurosurgery and epilepsy at Stanford University directed by Dr. Jaimie Henderson. 

Dr. Willsey will begin as an assistant professor of neurosurgery and biomedical engineering at the University of Michigan in August. His research involves investigating intracortical brain-computer interfaces to restore fine motor control in human participants with paralysis. His clinical interests include deep brain stimulation, MR-guided focused ultrasound, epilepsy, pain. His research interests include brain-computer interfaces, neuromodulation, and computational neuroscience. 

Matt Mender will join the lab in August 2024 as a Research Fellow. Previously, he completed his PhD in Biomedical Engineering at the University of Michigan with Dr. Cindy Chestek. During his PhD he studied brain-machine interface algorithms, asking how well they generalize to task changes, and also worked towards restoring hand movements with functional electrical stimulation. Prior to his PhD he attended the University of Rochester and worked at Epic Systems.

Shikhar Gupta is a Master’s student at the University of Texas at Austin pursuing Computer Science. He also completed his undergraduate Bachelor’s degree in Computer Science and Math with a certificate in Applied Statistical modeling from UT Austin. He has worked previously at Amazon as a software engineer intern, and his research interests primarily lie in robotics, machine learning, and neural networks in Brain Machine interfaces.