OSC Colloquium: George Barbastathis

    Date: 
    Thursday, August 27, 2020 - 3:30pm - 5:00pm
    Event Video: 
    Link
    Location: 
    Virtual Via Zoom
    Registration: 

    Open to campus and public.

    Description: 

    Speaker: George Barbastathis

    Topic: AI-enhanced vision: seeing the invisible

    Visit our website for future lecture dates and speaker information: http://www.optics.arizona.edu/news-events/events/colloquium For a list of our archived lectures: http://www.optics.arizona.edu/news-events/events/colloquium/archive

    Abstract(s): 
    If you point your camera to a scene, and the camera registers nothing—does it mean that
    nothing was really there? Hardly! The camera pixels measure “raw” light intensity where the
    encoded information often is much richer than a human observer could tell just by looking at
    the pixels on a screen. Which algorithms, then, should one apply to decode the raw intensity
    and reveal the hidden scene?
    In this seminar, I will describe how to use Deep Neural Networks (DNNs), a form of Machine
    Learning (ML) algorithm, to perform this decoding. During the training stage of the DNN,
    physically generated objects are used to produce the encoded raw intensities. From these pairs
    of objects and raw intensities the DNN learns the association between the scenes and their
    encoded representations. After training, given a new scene, the DNN decodes it correctly to
    produce a final reconstructed image that is meaningful to a human observer.
    With my research group, we applied this approach to three challenging instances of invisibility:
    transparent objects, also known as “phase objects,” whose raw intensities are highly rippled
    diffraction patterns; phase objects that are also very dark, i.e. the diffraction patterns are also
    highly attenuated; and objects hidden behind or surrounded by diffusers, e.g. frosted glass or
    multiple layers of glass patterned with sharp light-scattering features.
    It is important to emphasize that in our work ML is not used in the traditional way to interpret
    the scenes; rather, it is used to form interpretable representations of scenes in situations where
    traditional ML would be helpless due to physical limitations in the optics. The cooperation of
    ML with physical models proved to be very powerful in this work and, beyond, is certain to
    impact many fundamental and applied aspects of physical and life sciences and engineering.
    Speaker Bio(s): 
    George Barbastathis received the Diploma in Electrical and Computer Engineering in 1993 from the National Technical University of Athens (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 Professor of Mechanical Engineering. He has worked or held visiting appointments at Harvard University, the Singapore-MIT Alliance for Research and Technology (SMART) Centre, the National University of Singapore, and the
    University of Michigan – Shanghai Jiao Tong University Joint Institute (密西根交大學院) in Shanghai, People’s Republic of China. His research interests are in machine learning and optimization for computational imaging and inverse problems; and optical system design, including artificial optical materials and interfaces. He is member of the Society for Photo Instrumentation Engineering (SPIE), the Institute of Electrical and Electronics Engineering (IEEE), and the American Society of Mechanical Engineers (ASME). In 2010 he was elected Fellow of the Optical Society of America (OSA) and in 2015 he was a recipient of China’s Top Foreign Scholar (“One Thousand Scholar”) Award.
    Schedule: 

    Colloquium will start at 3:30pm, following will be a meet and greet with George Barbastathis.