Professor Vishal Patel
Title: Active User Authentication on Mobile Devices
Abstract: Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in use of mobile devices, one has to constantly worry about the security and privacy as the loss of a mobile device would compromise personal information of the user. To deal with this problem, active authentication (also known as continuous authentication) systems have been proposed in which users are continuously monitored after the initial access to the mobile device. This tutorial will provide an overview of different continuous authentication methods on mobile devices. We will discuss merits and drawbacks of available approaches and identify promising avenues of research in this rapidly evolving field. The tutorial should prove valuable to security and biometrics experts, exposing them to opportunities provided by continuous authentication approaches. It should also prove beneficial to experts in computer vision and signal processing, introducing them to a different tool with very interesting research problems.
Bio: Vishal M. Patel is an A. Walter Tyson Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. Prior to joining Rutgers University, he was a member of the research faculty at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He completed his Ph.D. in Electrical Engineering from the University of Maryland, College Park, MD, in 2010. His current research interests include signal processing, computer vision, and pattern recognition with applications in biometrics and imaging. He has received a number of awards including the 2016 ONR Young Investigator Award, the 2016 Jimmy Lin Award for Invention, A. Walter Tyson Assistant Professorship Award, Best Paper Award at IEEE AVSS 2017, Best Paper Award at IEEE BTAS 2015, and Best Poster Awards at BTAS 2015 and 2016. He is an Associate Editor of the IEEE Signal Processing Magazine, IEEE Biometrics Compendium, and serves on the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. He is a member of Eta Kappa Nu, Pi Mu Epsilon, and Phi Beta Kappa.
Professor Xavier Bresson
Title: Convolutional Neural Networks on Graphs
Abstract: Convolutional neural networks have greatly improved state-of-the-art performances in computer vision and speech analysis tasks, due to its high ability to extract multiple levels of representations of data. In this tutorial, we are interested in generalizing convolutional neural networks from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, telecommunication networks, or brain connectivity networks. We present a formulation of convolutional neural networks on graphs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Applications are given in computer vision, natural language processing, and recommender systems.
Bio: Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science and member of the Data Science and AI Research Centre at NTU, Singapore. He is a leading researcher in the field of graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in neuroscience, genetics, social science, physics, and natural language processing. He received in 2016 the highly competitive Singaporean NRF Fellowship of 2.5M US$ to develop these new techniques. He has organized international workshops and tutorials with Facebook, NYU, and USI about this emerging field such as the 2018 UCLA workshop, the 2017 CVPR tutorial, and the 2017 NIPS tutorial. He has published more than 60 peer-reviewed papers, including NIPS, ICML, JMLR, the top venues in machine learning, https://scholar.google.ch/citations?hl=en&user=9pSK04MAAAAJ&view_op=list_works&sortby=pubdate. He was awarded several research grants in U.S. and Hong Kong. He has multiple consulting experiences with e.g. Nestle to design industrial deep learning techniques.