Orazio Gallo - NVIDIA Research, USA

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.

Title: A Few Learnings on Computational Photography with Deep Learning

Ryoichi Horisaki - Osaka University, Japan

Ryoichi Horisaki is an assistant professor at Osaka University and a Sakigake researcher at PRESTO. He is working mainly on computational imaging. He received a PhD from Osaka University in 2010.

Title: Computational imaging with randomness

Abstract: The talk will focus on research activities about optics-oriented computational imaging related to machine learning and compressive sensing.

Ulugbek Kamilov - Washington University in St. Louis, USA

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.

Title: Computational Imaging: Reconciling Models and Learning in Scalable Algorithms [Slides]

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..

Title: Geometry of deep learning for inverse problems

Leslie Ying - University at Buffalo, The State University of New York

Leslie Ying is the Furnas Chair Professor of Biomedical Engineering and Electrical Engineering at University at Buffalo, the State University of New York. Her research interests include magnetic resonance imaging, compressed sensing, image reconstruction, and machine learning. She received a CAREER award from the National Science Foundation in 2009. She was elected as an Administrative Committee member of IEEE Engineering in Medicine and Biology Society in 2013-2015. She was an Associate Editor of IEEE Transactions on Biomedical Engineering, and is now a Deputy Editor of Magnetic Resonance in Medicine and an editorial board member of Scientific Reports. She will be the Editor-in-Chief of IEEE Transactions on Medical Imaging in 2020.

Title: Machine Learning in Biomedical Image Reconstruction [Slides]