Regular biography
Franca Hoffmann is an Assistant Professor of Computing and Mathematical Sciences at the California Institute of Technology, Department of CS. She holds a B.S. from Imperial College London (2010), an M.S. (2013), and a Ph.D. from the University of Cambridge (2017). Her research focuses on the interface between applied mathematics and data analysis, aiming to provide rigorous mathematical foundations for modeling tools used in applications. She has served as an Instructor at Caltech from 2017 to 2020 and as a Visiting Associate from 2020 to 2022. Her teaching includes courses such as CMS 290 abc and ACM 256.
Scholar-generated biography
Franca Hoffmann is a researcher at the California Institute of Technology, focusing on mathematical and computational methods for data analysis and optimization. Her work explores the geometric and statistical structures underlying complex systems, including diffusion processes, consensus-based sampling, and mean-field limits. She investigates the interplay between stochastic dynamics and large-scale data modeling, with applications in machine learning, signal processing, and physical systems. Hoffmann's research also addresses the theoretical foundations of optimization algorithms, such as the ensemble Kalman sampler and semi-supervised learning on graphs. Her contributions span across probability theory, partial differential equations, and applied mathematics, emphasizing rigorous analysis and scalable computational techniques.