Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Director of the Mathematical Institute for Data Science, the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning, and the NSF TRIPODS Institute on the Foundations of Graph and Deep Learning at The Johns Hopkins University. He has secondary appointments in Applied Mathematics and Statistics, Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). He is also an Amazon Scholar, Chief Scientist at NORCE, and Associate Editor in Chief of TPAMI. Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and counting of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, assessment of surgical skill in kinematic and video data. His lab also develops computer vision technology for pedriatric rehabilitation therapy, autism, and Tourette syndrome.