Joan Bruna

Joan Bruna is a Professor of Computer Science, Data Science and Mathematics at New York University, affiliated with the Courant Institute and the Center for Data Science. She is a member of the CILVR group and co-founded the MaD group. Her research spans Machine Learning, Signal Processing, and High-Dimensional Statistics, with a focus on the mathematical foundations of ML, including optimization, representation, and statistical aspects. She is also a Visiting Scholar at the Flatiron Institute and has spent time at the Institute for Advanced Study. Her work applies computational methods to fields such as geophysics and climate modeling.

Joan Bruna is a Professor of Computer Science, Data Science & Mathematics at the Courant Institute and CDS, NYU. Her research focuses on Machine Learning, with an emphasis on geometric deep learning and the development of novel architectures for processing non-Euclidean data. Bruna's work explores the application of graph neural networks, convolutional networks on graphs, and the geometric properties of neural networks. She has contributed to areas such as invariant scattering convolution networks, stability properties of graph neural networks, and the use of geometric principles in deep learning. Her research also includes topics like few-shot learning, community detection, and the analysis of neural network optimization.