Assistant Professor Vivek Goyal Information Sciences and Systems Boston University --First-Photon Imaging and Nonlinear Inverse Problems-- Abstract: For centuries, information acquisition technologies have depended first and foremost on obvious correspondences between physical measurements and quantities of interest. For example, lenses, mirrors, and photochemical measurement of optically focused light are the basis for photography. Today, imaging and other acquisition technologies are in the midst of a revolution whereby the measured quantities may have weak and unclear relationships with the quantities of interest. The measurements are useful only after solving an inverse problem, and solving these problems benefits from an integrated view of signal modeling, physical device modeling, and estimation algorithms. To concentrate on one illustration, I will present first-photon imaging (FPI). With FPI, we are able to use conventional LIDAR acquisition equipment to estimate range (with resolution finer than pulse duration) and reflectivity (with 4-bit resolution) from only one detected photon per pixel, even in the presence of significant ambient light. This contrasts with conventional techniques requiring at least hundreds of detected photons at each image pixel. I will more briefly cover our compressive depth acquisition camera (CoDAC) architecture, which captures depth images of simple scenes using a single, omnidirectional, time-resolved photodetector and no scanning components. In this system, a nonlinear aspect of the inverse problem is overcome through parametric modeling of the optical impulse response of the scene. Finally, I will summarize analytical results on performance achieved in solving large, randomized linear inverse problems. Biography: Vivek Goyal is an Assistant Professor of Electrical and Computer Engineering at Boston University. He was a Member of Technical Staff in the Mathematics of Communications Research Department of Bell Laboratories and a Senior Research Engineer for Digital Fountain before joining the faculty of the Massachusetts Institute of Technology and then Boston University. His research interests include computational imaging, decision making, quantization, sampling, and source coding theory. Dr. Goyal received the John Briggs Memorial Award at the University of Iowa for the top undergraduate across all colleges. He received the M.S. and Ph.D. degrees in electrical engineering from the University of California, Berkeley, where he received the Eliahu Jury Award for outstanding achievement in systems, communications, control, or signal processing. He was awarded the 2002 IEEE Signal Processing Society Magazine Award and an NSF CAREER Award. As a research supervisor, he is co-author of papers that won student best paper awards at IEEE Data Compression Conference in 2006 and 2011 and IEEE Sensor Array and Multichannel Signal Processing Workshop in 2012. His students have won MIT thesis awards in 2006, 2008, 2010, and 2013. He served on the IEEE Signal Processing Society's Image and Multiple Dimensional Signal Processing Technical Committee 2003-2009 and on the Steering Committee of the IEEE Transactions on Multimedia in 2013. He currently serves on the Editorial Board of Foundations and Trends in Signal Processing and on the Scientific Advisory Board of the Banff International Research Station for Mathematical Innovation and Discovery. He is Technical Program Committee Chair of IEEE ICIP 2016 and SampTA 2015 and a permanent Conference Co-chair of the SPIE Wavelets and Sparsity conference series. His "Foundations of Signal Processing" textbook, coauthored with M. Vetterli and J. Kovacevic, was published by Cambridge University Press this year. *Light beverages and snacks will be available at 3:45 pm