报告题目：Powering the Future of Signal Processing and Imaging with Data-Driven Systems
报 告 人：Dr. Saiprasad Ravishankar
美国密西根大学(University of Michigan - Ann Arbor), 博士后
报告时间：2018年6月27日 上午9:30
报告地点：清华大学刘卿楼402
主 持 人：高河伟，副教授
报告摘要：
The next generation imaging systems are expected to be increasingly data-driven. In this talk, I will present my research on efficient, scalable, and effective data-driven models and learning methodologies for signal processing and imaging. First, I will discuss transform learning, where interesting structures such as union-of-transforms, incoherence, rotation invariance, etc., can be considered. Transform learning-driven approaches achieve high-quality results in applications such as video denoising, and X-ray computed tomography and magnetic resonance image (MRI) reconstruction from limited or corrupted data. The convergence properties of the learning-based algorithms will be discussed. I will also present work on dictionary learning in combination with low-rank models (LASSI) and demonstrate its promise for dynamic MRI. The efficiency and effectiveness of the methods proposed in my research may benefit a variety of additional applications in imaging, computer vision, neuroscience, and other areas requiring data-driven parsimonious models. Finally, I will provide an overview of recent research and future pathways, including on physics-driven deep training of reconstruction algorithms, learning undersampling patterns in compressed sensing-type setups, online adaptive estimation of dynamic data from streaming measurements, etc.

报告人简介：
Saiprasad Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras in 2008, and 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 then an Adjunct Lecturer and a Postdoctoral Research Associate. Since August 2015, he is a postdoc at the University of Michigan. His interests include signal, image and video processing, imaging, machine learning, inverse problems, compressed sensing, and large-scale data processing and optimization. He has over 1500 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 2016.