Finding accurate neural networks for a specific scientific dataset or particular problem requires searching among thousands of neural networks on a large number of high-performance computing (HPC) resources. That could soon change thanks to research in HPC and AI being led by Michela Taufer.

EECS Assistant Professor Catherine Schuman was a featured speaker at last week’s Grace Hopper Celebration of Women in Computing in Orlando, Florida, relating how she and her group of collaborators used neuromorphic computing to control a small, autonomous model race car.

Assistant Professor of Computer Science Catherine Schuman mentored two recent high school graduates and a current high school student as they worked on neuromorphic computing projects together. The students presented a poster for their work at a neuromorphic computing conference.