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  • Powering the Future of Imaging and Signal Processing with Data-Driven Systems

    Fri, Sep 29, 2017 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Saiprasad Ravishankar, Electrical Engineering & Computer Science Department, University of Michigan

    Talk Title: Powering the Future of Imaging and Signal Processing with Data-Driven Systems

    Series: Medical Imaging Seminar Series

    Abstract: The data-driven learning of signal models including dictionaries, sparsifying transforms, low-rank models, tensor and manifold models, etc., is of great interest in many applications. In this talk, I will present my research that developed efficient, scalable, and effective data-driven models and methodologies for signal processing and imaging. I will mainly discuss my work on transform learning. Various interesting structures for sparsifying transforms such as well-conditioning, double sparsity, union-of-transforms, incoherence, rotation invariance, etc., can be considered, which enable their efficient and effective learning and usage. Transform learning-driven approaches achieve promising results in applications such as image and video denoising, and X-ray computed tomography or magnetic resonance image (MRI) reconstruction from limited or corrupted data. The convergence properties of the algorithms will be discussed. I will also present recent work on efficient dictionary learning in combination with low-rank models, and demonstrate the usefulness of the resulting LASSI method for dynamic MRI. The efficiency and effectiveness of the methods proposed in my research may benefit a wide range of additional applications in imaging, computer vision, neuroscience, and other areas requiring data-driven parsimonious models. Finally, I will provide a brief overview of recent works on physics-driven deep training of image reconstruction algorithms, light field reconstruction from focal stacks, online data-driven estimation of dynamic data from streaming, limited measurements, etc.

    Biography: Saiprasad Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, in 2008. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 2010 and 2014 respectively, from the University of Illinois at Urbana-Champaign, where he was an Adjunct Lecturer in the Department of Electrical and Computer Engineering during Spring 2015, and a Postdoctoral Research Associate at the Coordinated Science Laboratory until August, 2015. Since then, he has been a Research Fellow in the Electrical Engineering and Computer Science Department at the University of Michigan. His research interests include signal, image and video processing, signal modeling, data science, dictionary learning, biomedical and computational imaging, data-driven methods, inverse problems, compressed sensing, machine learning, and large-scale data processing.He has received multiple awards including the Sri Ramasarma V Kolluri Memorial Prize from IIT Madras and the IEEE Signal Processing Society Young Author Best Paper Award for his paper Learning Sparsifying Transforms published in IEEE Transactions on Signal Processing.


    Host: Professor Richard Leahy

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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