Organizers
Workshop Organizers
Bihan Wen - Nanyang Technological University, Singapore
Bihan Wen is currently a Nanyang Assistant Professor at Nanyang Technological University. He received the B.Eng. degree in Electrical and Electronic Engineering (EEE) from Nanyang Technological University (NTU), Singapore, in 2012, the MS and PhD degrees in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (UIUC), USA, in 2015 and 2018, respectively. His research interests span areas of machine learning, computational imaging, computer vision, image and video processing, and big data applications.
He is a member of the IEEE Computational Imaging (CI) Technical Committee. He regularly serves as the area chair for ICIP, ICASSP and ICME, and as the program committees or reviewers for computer vision and machine learning conferences (e.g., CVPR, ICCV, NeurIPS, IJCAI, AAAI). He also co-organized the CSLSC 2017 and MIPR 2019 as the Session Chairs. He was the recipient of the 2016 Yee Fellowship, and the 2012 Professional Engineers Board (PEB) Gold Medal.
Saiprasad Ravishankar - Michigan State University, USA
Saiprasad Ravishankar is currently an Assistant Professor in the Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University. He received the B.Tech. degree in Electrical Engineering from IIT Madras, India, 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 was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory. His interests include signal and image processing, biomedical and computational imaging, machine learning, inverse problems, and large-scale data processing and optimization. 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 2016 for his paper "Learning Sparsifying Transforms" published in the IEEE Transactions on Signal Processing. A paper he co-authored won a best student paper award at the IEEE International Symposium on Biomedical Imaging, 2018, and another was a finalist at the IEEE International Workshop on Machine Learning for Signal Processing, 2017. He is currently a member of the IEEE Computational Imaging Technical Committee. He has organized special sessions on computational imaging themes at the IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop 2016, the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, and the IEEE International Symposium on Biomedical Imaging (ISBI) 2018.
Singanallur Venkatakrishnan received the B.Tech. degree in electronics and communication engineering from the National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India, in 2007, and the M.S. and Ph.D. degrees in electrical and computer engineering from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA, in 2009 and 2014, respectively. He was subsequently a Postdoctoral Fellow with the Lawrence Berkeley National Laboratory, Berkeley, CA, USA, affiliated with the Advanced Light Source and the Center for Applied Mathematics for Energy Research Applications developing reconstruction algorithms for X-ray scattering and tomography. He is currently an R&D Staff Member and a Eugene P. Wigner Distinguished Fellow with the Imaging, Signals and Machine Learning Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA, developing computational imaging algorithms in support of the lab's efforts in ultrasound, X-ray, electron, and neutron based systems. His research interests include computational imaging, inverse problems, and machine learning. Dr. Venkatakrishnan was awarded a Presidential Scholar Award at the Microscopy and Microanalysis Conference (2014) for his work on the development of an algorithm for low-dose electron tomography.
Ulugbek S. Kamilov is an Assistant Professor and Director of Computational Imaging Group (CIG) at Washington University in St. Louis. His research area is computational imaging with an emphasis on biomedical applications, including optical microscopy, MRI, and tomographic imaging. His research interests include signal and image processing, large-scale optimization, machine learning, and statistical inference. He obtained the BSc and MSc degrees in Communication Systems, and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2008, 2011, and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. He is a recipient of the IEEE Signal Processing Society’s 2017 Best Paper Award (with V. K. Goyal and S. Rangan). His Ph.D. thesis was selected as a finalist for the EPFL Doctorate Awardin 2016. His work on Learning Tomography (LT) was featured in Nature “News and Views” in 2015. He is a member of IEEE Technical Committee on Computational Imaging since 2016.
Orazio Gallo is a Principal Research Scientist at NVIDIA Research. He is interested in computational imaging, computer vision, deep learning and, in particular, in the intersection of the three. Alongside topics such as view synthesis and 3D vision, his recent interests also include integrating traditional computer vision and computational imaging knowledge into deep learning architectures. Previously, Orazio research focus revolved around tinkering with the way pictures are captured, processed, and consumed by the photographer or the viewer. Orazio is an associate editor of the IEEE Transactions of Computational Imaging and was an associate editor of Signal Processing: Image Communication from 2015 to 2017. Since 2015 he is also a member of the IEEE Computational Imaging Technical Committee.
Katie Bouman is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor's degree in Electrical Engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.
Brendt Wohlberg - Los Alamos National Laboratory, USA
Brendt Wohlberg received the BSc (Hons) degree in Applied Mathematics, the MSc (Applied Science) in Applied Science, and the Ph.D. degree in Electrical Engineering from the University of Cape Town, South Africa, in 1990, 1993, and 1996, respectively. He is currently a Staff Scientist with the Theoretical Division at Los Alamos National Laboratory, Los Alamos, NM, USA. His primary research interest is in regularization methods for signal and image processing inverse problems. He was an Associate Editor for the IEEE Transactions On Image Processing from 2010 to 2014, and for the IEEE Transactions On Computational Imaging from 2015 to 2017, and was a Chair of the Computational Imaging Special Interest Group (now the Computational Imaging Technical Committee) of the IEEE Signal Processing Society from 2015 to 2017. He was technical program co-chair of the 2018 IEEE Image, Video, and Multidimensional Signal Processing Workshop, and has co-organized a number of special sessions on computational imaging at IEEE and SIAM conferences and workshops. He is currently Editor-in-Chief of the IEEE Transactions on Computational Imaging, and an Associate Member of the Computational Imaging Technical Committee.
Jong Chul Ye - Korea Advanced Institute of Science and Technology, Korea
Jong Chul Ye is a Professor of the Dept. of Bio/Brain Engineering and Adjunct Professor at Dept. of Mathematical Sciences of Korea Advanced Institute of Science and Technology (KAIST), Korea. He received the B.Sc. and M.Sc. degrees from Seoul National University, Korea, and the Ph.D. from Purdue University, West Lafayette. Before joining KAIST, he was a Senior Researcher at Philips Research, GE Global Research in New York, and a postdoctoral fellow at University of Illinois at Urbana Champaign. He has served as an associate editor of IEEE Trans. on Image Processing, IEEE Trans. on Computational Imaging, and an editorial board member for Magnetic Resonance in Medicine. He is currently an associate editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine. He is an IEEE Fellow, Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer. He was a General Co-chair for 2020 IEEE Symp. On Biomedical Imaging (ISBI) (with Mathews Jacob), and is a Program Co-Chair for 2024 ICASSP. His group was the first winner of the 2009 Recon Challenge at the ISMRM workshop with k-t FOCUSS algorithm, and the runner-up at 2016 Low Dose CT Grand Challenge organized by the American Association of Physicists in Medicine (AAPM) with the world’s first deep learning algorithm for low-dose CT reconstruction. His current research interests focus is deep learning theory and algorithms for various imaging reconstruction problems in x-ray CT, MRI, optics, ultrasound, remote sensing, etc.