Speaker: George Barbastathis (MIT)

Title: Darker, smaller, faster: Machine learning regularizers for imaging at the extremes 

Abstract: The notion of data-driven regularizers for ill-posed imaging has been around since at least the invention of “dictionaries” by Donoho and Elad in 2003. Deep neural networks, as in many other application domains, have enhanced the scope that this notion can be useful.  

For the past 9 years, my group has been working on imaging at extreme conditions, which include: low photon incidence, down to a single photon per pixel; strong attenuation and scattering, as occurs to coherent x-rays (generated by a synchrotron) when they propagate through complex objects such as integrated circuits; and, more recently, phenomena faster than the camera frame rate and with motion range that is a fraction of a pixel, i.e. severely undersampled in both space and time domains. I will present and critique the methods that we used to make progress in these difficult problems, followed by a more general outlook on how machine learning-inspired methods can be useful in the physical sciences and engineering.  

Biography: George Barbastathis received the Diploma in Electrical and Computer Engineering in 1993 from the National Technical University of Athens (Εθνικό Μετσόβιο Πολυτεχνείο) and the MSc and PhD degrees in Electrical Engineering in 1994 and 1997, respectively, from the California Institute of Technology (Caltech). After post-doctoral work at the University of Illinois at Urbana-Champaign, he joined the faculty at MIT in 1999, where he is now the Ralph E. and Eloise F. Cross Professor of Manufacturing and Professor of Mechanical Engineering. He has held sabbatical appointments at Harvard University and the University of Michigan – Shanghai Jiao Tong University Joint Institute (密西根交大学院), and has been one of the longest-serving Principal Investigators with the Singapore-MIT Alliance for Research and Technology (SMART). His research interests are in machine learning and optimization for computational imaging and inverse problems; and in optical physics, including statistical optics, scattering theory, and artificial optical materials and interfaces. He is presently leading the AFOSR MURI “Searching for what’s new: the systematic development of dynamic x-ray microscopy,” whose goal is to combine dynamical principles and machine learning for imaging extreme phenomena with sub-microsecond and sub-nanometer dynamics. He is member of the Institute of Electrical and Electronics Engineering (IEEE) and the American Mathematical Society (AMS), a Fellow of the Optical Society of America (OSA), which was recently rebranded as Optica, and a Fellow of the Society for Photo Instrumentation Engineering (SPIE). He has served as Associate Editor for the Journal of the Optical Society of America A and the journal Optica, as a committee member, chair and co-chair of numerous conferences, and in several co-founding and consulting capacities for incumbent and start-up companies, as well as law firms. In his free time, he enjoys arthouse movies, urban art and design, and he aspires to become more fluent in Thai and Mandarin Chinese.