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, and serves on the program committees or reviewers of the top 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.

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 currently KAIST Endowed Chair Professor and 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 worked at Philips Research and GE Global Research in New York. He has served as an associate editor of IEEE Trans. on Image Processing, and an editorial board member for Magnetic Resonance in Medicine. He is currently an associate editor for IEEE Trans. on Medical Imaging, IEEE Trans. on Computational Imaging, and Journal of Electronic Imaging, and an international advisory board member for Physics in Medicine and Biology. He is also a Senior Editor of IEEE Signal Processing Magazine. He is an elected member of IEEE SPS Technical Committee on Bio-imaging and Signal Processing (BISP), IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP), and IEEE SPS Special Interest Group (SiG) on Computational Imaging, and a Technical Liaison Committee of IEEE Trans. on Computational Imaging. He is/was on the organizing committee for IEEE Symp. on Biomedical Imaging (ISBI) 2006, 1st ISMRM Workshop on Machine Learning 2018, and International BASP Frontiers Workshop 2019. He is/was a tutorial/keynote/plenary speaker in various conferences including ISBI, ISMRM, SPIE Medical Imaging, CT Meeting, MICCAI Workshop, IFMIA, etc. His group was the first place winner of the 2009 Recon Challenge at the ISMRM workshop with k-t FOCUSS algorithm, the second winners 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, and the third place winner for 2017 CVPR NTIRE challenge on example-based single image super-resolution. He was an advisor of student’s best paper awards (1st, and runner-up) at 2013 and 2016 IEEE Symp. on Biomedical Imaging (ISBI). His current research interests include machine learning, compressed sensing and statistical signal processing for various image reconstruction problems in various medical and bioimaging modalities such as MRI, CT, optics, ultrasound imaging, PET, fNIRS, etc..