Nina Miolane

Nina Miolane is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of California, Santa Barbara (UCSB). She is affiliated with the Geometric Intelligence Lab and the Center for Aging and Longevity Studies (CALS). Her research focuses on Geometric Artificial Intelligence (Geometric AI), combining machine learning with geometric and topological principles to study brain health, neural networks, and biological systems. Miolane’s work spans AI-driven medical imaging analysis, Riemannian geometry applications, and the development of interpretable neural network models.

Her research interests include topological deep learning, geometric deep learning, computational neuroscience, and shape analysis in biosciences. She leads initiatives in developing frameworks like TopoX and Geomstats, which enable machine learning on non-Euclidean domains. Miolane’s projects often intersect with aging research, leveraging AI to understand brain health through MRI data and neural recordings.

Publications highlight contributions in manifold optimization, topological neural networks, and applications in cancer cell morphology analysis. She has received grants such as the NSF CAREER Award for advancing shape learning in biosciences. Miolane collaborates across disciplines, integrating mathematics, computer science, and biomedical engineering to advance interpretable AI systems.