Jessica Hoffmann

Jessica Hoffmann works on epidemic processes on graphs, with a focus on noisy observation models (such as those in which the infected status of nodes is uncertain or the time of infection is noisy).

Her research interests include: epidemic processes, graph theory, high-dimensional statistics, machine learning, and applications to large-scale networks. She is also interested in convex and nonconvex optimization, robust statistics, and learning theory.

She received her Master’s degree from Ecole Normale Supérieure in Paris and her Ph.D. from the University of Texas at Austin.