Date(s) - 3 Nov 2017
1:10 PM - 2:00 PM
3043 ECpE Building Addition
Title: Powering the Future of Signal Processing and Imaging with Data-Driven Systems
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 various efficient, scalable, and effective data-driven models and methodologies for signal processing and imaging. I will mainly discuss my work in the recently developed field of 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 high-quality 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 learning-based algorithms will be briefly discussed. I will also present recent work on efficient synthesis 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 and future pathways for my research. This will include topics such as physics-driven deep training of image reconstruction algorithms, data-driven learning of undersampling patterns in compressed sensing-type setups, light field reconstruction from focal stacks, online data-driven estimation of dynamic data from streaming, limited measurements, etc.
Bio: 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 over 1200 Google Scholar citations and 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. He has organized several sessions at IEEE conferences and workshops on up and coming research themes including one in ISBI 2018 on smart imaging systems.