Invited Speakers
Invited Speakers - Season 3, 2021
Qing Qu - University of Michigan – Ann Arbor, USA
Qing Qu is an assistant professor in EECS department at the University of Michigan. Prior to that, he was a Moore-Sloan data science fellow at Center for Data Science, New York University, from 2018 to 2020. He received his Ph.D from Columbia University in Electrical Engineering in Oct. 2018. He received his B.Eng. from Tsinghua University in Jul. 2011, and a M.Sc.from the Johns Hopkins University in Dec. 2012, both in Electrical and Computer Engineering. He interned at U.S. Army Research Laboratory in 2012 and Microsoft Research in 2016, respectively. His research interest lies at the intersection of foundation of data science, machine learning, numerical optimization, and signal/image processing, with focus on developing efficient nonconvex methods and global optimality guarantees for solving representation learning and nonlinear inverse problems in engineering and imaging sciences. He is the recipient of Best Student Paper Award at SPARS’15 (with Ju Sun, John Wright), and the recipient of Microsoft PhD Fellowship in machine learning.
Title: From Shallow to Deep Representation Learning in Imaging and Beyond: Global Nonconvex Theory and Algorithms
Talk 1 [Date: Tuesday, Sep 7, 2021 ]
Bin Dong is a faculty member of the Beijing International Center for Mathematical Research at Peking University. He received his B.S. from Peking University in 2003, M.Sc from the National University of Singapore in 2005, and Ph.D. from the University of California Los Angeles in 2009. He received the Qiu Shi Outstanding Young Scholar Award in 2014 and was invited to deliver a 45-minute sectional lecture at the International Congress of Mathematicians (ICM) 2022. Bin Dong's research interest is in the mathematical foundations of image and data analysis and its applications. This includes mathematical analysis, modeling, and computations in image processing, medical imaging, and deep learning.
Title: Data- and Task-Driven CT Imaging by Deep Learning
Talk 2 [Date: Tuesday, Sep 21, 2021 ]
Wolfgang Heidrich is a Professor of Computer Science and the Director of the Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Prof. Heidrich joined KAUST in 2014, after 13 years as a faculty member at the University of British Columbia. He received his Ph.D. from the University of Erlangen in 1999, and then worked as a Research Associate in the Computer Graphics Group of the Max-Planck-Institute for Computer Science in Saarbrucken, Germany, before joining UBC in 2000. Prof. Heidrich’s research interests lie at the intersection of imaging, optics, computer vision, computer graphics, and inverse problems. His more recent interest is in computational imaging, focusing on hardware-software co-design of the next generation of imaging systems, with applications such as High-Dynamic Range imaging, compact computational cameras, hyperspectral cameras, to name just a few. Prof. Heidrich’s work on High Dynamic Range Displays served as the basis for the technology behind Brightside Technologies, which was acquired by Dolby in 2007. Prof. Heidrich is a Fellow of the IEEE and Eurographics and the recipient of a Humboldt Research Award.
Title: Deep Optics — Joint Design of Imaging Hardware and Reconstruction Methods
Talk 3 [Date: Tuesday, Oct 5, 2021 ]
Salman Asif received the B.Sc. degree from the University of Engineering and Technology, Lahore, Pakistan, and the M.S. and Ph.D. degrees from the Georgia Institute of Technology, Atlanta.
He is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside. He previously worked as a Senior Research Engineer at Samsung Research America, Dallas, TX, and as a Postdoctoral Researcher at Rice University, TX. His research interests include computational imaging, signal/image processing, computer vision, and machine learning.
Dr. Asif received the NSF CAREER Award (2021), the Google Faculty Award (2019), the Hershel M. Rich Outstanding Invention Award (2016), and the UC Regents Faculty Fellowship (2017) and Development (2021) Awards.
Title: Lensless Imaging with Programmable Masks and Illumination
Talk 4 [Date: Tuesday, Nov 2, 2021 ]
Mathews Jacob received the B.Tech. degree in electronics and communication engineering from National Institute of Technology, Calicut, Kerala, and the M.E. in signal processing from the Indian Institute of Science, Bangalore. He received the Ph.D. degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology in 2003. He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign.
He is a professor in the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG) at the University of Iowa. His research interests include image reconstruction, image analysis, and quantification in the context of magnetic resonance imaging.
Dr. Jacob is the recipient of the CAREER award from the National Science Foundation in 2009, the Research Scholar Award from American Cancer Society in 2011, and the Faculty Excellence Award for Research from University of Iowa in 2021. He is currently the associate editor of the IEEE Transactions on Medical Imaging and has served as the associate editor of IEEE Transactions on Computational Imaging from 2016 to 2020. He was the senior author on two best paper awards (2015 and 2021) and one best machine learning paper award (2019) from IEEE ISBI. He was the general chair of IEEE International Symposium on Biomedical Imaging, 2020.
Title: Model-based Deep Learning for Large-scale Inverse Problems
Talk 5 [Date: Tuesday, Nov 30, 2021 ]
Se Young Chun is an Associate Professor of Department of ECE, Seoul National University (SNU), Republic of Korea and is also affiliated with Graduate School of AI, SNU. He received a BSE from School of EE (now Department of ECE), SNU. Then, he received dual MSE / MS in EE:systems / mathematics and a PhD in EE:systems from University of Michigan-Ann Arbor (UM-AA), USA. He was a Research Fellow at Mass. General Hospital / Harvard Medical School, USA, and then at EECS / Radiology, UM-AA, USA. Before joining SNU, he was with School of ECE and AI, UNIST, Republic of Korea as an Assistant / Associate Professor. His research interests are computational imaging algorithms using machine learning and statistical signal processing for applications in medical imaging, computer vision and robotics. Dr. Chun won the 2010 Society of Nuclear Medicine Computer & Instrumentation Young Investigator 2nd Place Award. He was also the recipient of the 2015 Bruce Hasegawa Young Investigator Medical Imaging Science Award from IEEE Nuclear and Plasma Sciences Society (NPSS). He is an Associate Editor of IEEE Transactions on Computational Imaging and won an IEEE Signal Processing Society Outstanding Editorial Board Member Award in 2021. He has served as an area chair of IEEE ICASSP 2018, 2020 and 2022. He is a member of IEEE Computational Imaging Technical Committee (TC) and a member of IEEE Bio Imaging & Signal Processing TC.
Title: Towards Deep Learning-Based Image Reconstruction With Model-Based Self-Supervision
Talk 5 [Date: Tuesday, Dec 14, 2021 ]
Invited Speakers - Season 2, 2021
YongKeun (Paul) Park is Professor of Physics at KAIST. He earned a Ph.D. in Medical Science and Medical Engineering from Harvard-MIT Health Science and Technology. Dr. Park’s area of research is optics, holography, and biophysics. He has published +160 peer-reviewed papers with +10K citations, including 4 Nat Photon, 4 Nat Comm, 4 PRL, 5 PNAS papers. He is a Fellow of Optical Society of America (OSA) and Society of Photo-Optical Instrumentation Engineers (SPIE). He received Medals of Honor in Science and Technology (President of South Korea) and JinkiHong Creative Award. Two start-up companies with +60 employees have been created from his research (Tomocube, The.Wave.Talk). To learn more about Prof. Park's research projects, visit his website: http://bmol.kaist.ac.kr.
Title: Quantitative phase imaging and artificial intelligence: label-free 3D imaging, classification, and inference
Talk 1 [Date: Tuesday, Feb 9, 2021 ]
Pier Luigi Dragotti - Imperial College London, UK
Pier Luigi Dragotti is Professor of Signal Processing in the Electrical and Electronic Engineering Department at Imperial College London and a Fellow of the IEEE. He received the Laurea Degree (summa cum laude) in Electronic Engineering from the University Federico II, Naples, Italy, in 1997; the Master degree in Communications Systems from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland in 1998; and the Ph.D. degree from EPFL, Switzerland, in 2002. He has held several visiting positions, in particular, he was a visiting student at Stanford University, Stanford, CA, in 1996, a summer researcher in the Mathematics of Communications Department at Bell Labs, Lucent Technologies, Murray Hill, NJ, in 2000, a visiting scientist at Massachusetts Institute of Technology (MIT), Cambridge, in 2011, and a visiting scholar at Trinity College, Cambridge, England, in 2020. Before joining Imperial College London in November 2002, he was a senior researcher at EPFL working on distributed signal processing for the Swiss National Competence Center in Research on Mobile Information and Communication Systems. His research interests include sampling theory, wavelet theory and its applications, computational imaging and sparsity-driven signal processing. Dr. Dragotti was Editor-in-Chief of the IEEE Transactions on Signal Processing (2018-2020), Technical Co-Chair for the European Signal Processing Conference in 2012, Associate Editor of the IEEE Transactions on Image Processing from 2006 to 2009. He was also Elected Member of the IEEE Computational Imaging Technical Committee and is the recipient of an ERC starting investigator award for the project RecoSamp. Currently, he is IEEE SPS Distinguished Lecturer.
Title: Computational Imaging for Art Investigation: Revealing Hidden Drawings in Leonardo’s Paintings
Talk 2 [Date: Tuesday, Feb 23, 2021 ]
Gordon Wetzstein - Stanford University, USA
Gordon Wetzstein is an Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, display, wearable computing, and microscopy systems. Prior to joining Stanford in 2014, Prof. Wetzstein was a Research Scientist in the Camera Culture Group at MIT. He received a Ph.D. in Computer Science from the University of British Columbia in 2011 and graduated with Honors from the Bauhaus in Weimar, Germany before that. He is the recipient of an NSF CAREER Award, an Alfred P. Sloan Fellowship, an ACM SIGGRAPH Significant New Researcher Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), an SPIE Early Career Achievement Award, a Terman Fellowship, an Okawa Research Grant, the Electronic Imaging Scientist of the Year 2017 Award, an Alain Fournier Ph.D. Dissertation Award, and a Laval Virtual Award as well as Best Paper and Demo Awards at ICCP 2011, 2014, and 2016 and at ICIP 2016.
Title: Towards Neural Signal Processing and Imaging
Talk 3 [Date: Tuesday, Mar 9, 2021 ]
Yonina Eldar - Weizmann Institute of Science, Israel
Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel, where the heads the center for biomedical engineering. She was previously a Professor in the Department of Electrical Engineering at the Technion, where she held the Edwards Chair in Engineering. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering both from Tel-Aviv University (TAU), Tel-Aviv, Israel, in 1995 and 1996, respectively, and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge, in 2002. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), the Award for Women with Distinguished Contributions, the Andre and Bella Meyer Lectureship, the Career Development Chair at the Technion, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion’s Award for Excellence in Teaching (two times). She received several best paper awards and best demo awards together with her research students and colleagues, was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing and a member of several IEEE Technical Committees and Award Committees.
Title: Model Based Deep Learning: Applications to Imaging and Communications
Talk 4 [Date: Wednesday, Mar 24, 2021 ]
Ivan Dokmanić - University of Basel, Switzerland
Ivan Dokmanić is an Associate Professor in the Department of Mathematics and Computer Science at the University of Basel, Switzerland. From 2016 to 2019, he was an Assistant Professor in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign, where he now holds an adjunct appointment. He received a diploma in electrical engineering from the University of Zagreb in 2007, the Ph.D. degree in computer science from EPFL in 2015, and completed a postdoctoral position at Institut Langevin and Ecole Normale Supérieure in Paris between 2015 and 2016. Before that he was a teaching assistant at the University of Zagreb, a codec developer for MainConcept AG, Aachen, and a digital audio effects designer for Little Endian Ltd., Zagreb. His research interests lie between inverse problems, machine learning, and signal processing. Dr. Dokmanić received the Best Student Paper Award at ICASSP 2011, a Google Ph.D. Fellowship, an EPFL Outstanding Doctoral Thesis Award, and a Google Faculty Research Award. In 2019, the European Research Council (ERC) awarded him a Starting Grant. A side note: He used to be the singer and the lead guitarist of Ivan and the Terribles, featuring Martin Vetterli on bass, Paolo Prandoni on everything, and Marta Martinez-Cámara on saxophone.
Title: Learning the Geometry of Wave-Based Imaging
Talk 5 [Date: Wednesday, Apr 6, 2021 ]
Ori Katz - Hebrew University of Jerusalem
Ori Katz is an Associate Professor at the Department of Applied Physics, Faculty of Natural Sciences, at the Hebrew University of Jerusalem, Israel. Ori has received his PhD in Physics from the Weizmann Institute of Science in 2011, where he studied temporal and spatial ultrashort pulse shaping for nonlinear microscopy and spectroscopy in complex media. Ori performed his postdoctoral research at Institut Langevin and Laboratoire Kastler Brossel in Paris, working on optical and photo-acoustic imaging in complex media. Ori is leading the Advanced Imaging Lab, whose research is focused on the challenge of high-resolution imaging and sensing in complex scattering media. This research, situated at the interface between physics and engineering utilizes tool from several different disciplines using both acoustics and optics, and advanced computational algorithms, to solve applied real-world problems, such as deep tissue imaging.
Title: Imaging with scattered light: Exploiting speckle to see deeper and sharper
Talk 6 [Date: Wednesday, Apr 20, 2021 ]
Lei Tian - Boston University, USA
Lei Tian is an Assistant Professor in Electrical and Computer Engineering department and leads the Computational Imaging Systems lab (http://sites.bu.edu/tianlab/) at Boston University. He received his Ph.D. (2013) and M.S. (2010) from MIT. He was a postdoctoral associate in the EECS department at University of California, Berkeley 2013-2016. His research focuses on computational microscopy, neurophotonics, imaging in complex media, and machine learning for biomedical microscopy.
Dr. Tian’s awards include NSF CAREER award, the 2018 Boston University Dean’s Catalyst Award, the 2018 SPIE Fumio Okano Best 3D Paper Prize, the 2014 OSA Imaging Systems and Applications Best Paper Award, and the 2011 OSA Emil Wolf Outstanding Student Paper Prize .
Title: Model and learning strategies for computational 3D phase microscopy
Talk 7 [Date: Wednesday, May 4, 2021 ]
Rebecca Willett - University of Chicago, USA
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. Her research is focused on machine learning, signal processing, and large-scale data science. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group, received an Air Force Office of Scientific Research Young Investigator Program award in 2010, and was named a Fellow of the Society of Industrial and Applied Mathematics in 2021. She is a co-principal investigator and member of the Executive Committee for the Institute for the Foundations of Data Science, helps direct the Air Force Research Lab University Center of Excellence on Machine Learning, and currently leads the University of Chicago’s AI+Science Initiative. She serves on advisory committees for the National Science Foundation’s Institute for Mathematical and Statistical Innovation, the AI for Science Committee for the US Department of Energy’s Advanced Scientific Computing Research program, the Sandia National Laboratories Computing and Information Sciences Program, and the University of Tokyo Institute for AI and Beyond. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.
Title: Machine Learning and Inverse Problems in Imaging
Talk 8 [Date: Tuesday, May 18, 2021 ]
Marvin M. Doyley - University of Rochester
Marvin M. Doyley received his Ph.D. degree in biophysics from the Institute of Cancer Research (Sutton), University of London in 2000. In 2008, he joined the faculty of the Department of Electrical and Computer Engineering at the University of Rochester in Rochester New York. Dr. Doyley is currently Department Chair and Professor in the Department of Electrical and Computer Engineering, with joint appointments in the Departments of Biomedical Engineering and Imaging Sciences. His Parametric Imaging Research Laboratory at the University of Rochester concentrates their efforts in the areas of non-invasive vascular imaging, model-based intravascular ultrasound elastography, and high frequency nonlinear ultrasound imaging. Dr. Doyley is a fellow of the American Institute for Medical and Biological Engineering (AIMBE). He currently serves on the editorial boards of IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, SPIE Journal of Medical Imaging, Physics in Medicine and Biology, and Nature Scientific Reports.
Title: Elastography from theory to practice
Talk 9 [Date: Tuesday, June 1, 2021 ]
Sabine Süsstrunk is full professor and director of the Image and Visual Representation Lab in the School of Computer and Communication Sciences (IC) at the Ecole Polytechnique Fédérale (EPFL), Lausanne, Switzerland, since 1999. Her main research areas are in computational photography, computational imaging, color image processing and computer vision, machine learning, and computational image quality and aesthetics. Sabine is President of the Swiss Science Council SSC, Founding Member and Member of the Board (President 2014-2018) of the EPFL-WISH (Women in Science and Humanities) Foundation, Member of the Board of the SRG SSR (Swiss Radio and Television Corporation), and Co-Founder and Member of the Board of Largo Films. She received the IS&T/SPIE 2013 Electronic Imaging Scientist of the Year Award for her contributions to color imaging, computational photography, and image quality, and the 2018 IS&T Raymond C. Bowman and the 2020 EPFL AGEPoly IC Polysphere Awards for excellence in teaching. Sabine is a Fellow of IEEE and IS&T.
Title: Opponency Revisted
Talk 10 [Date: Tuesday, June 23, 2021 ]
Invited Speakers - Season 1 2020
Raja Giryes - Tel Aviv University, Israel
Raja Giryes is an assistant professor in the school of electrical engineering at Tel Aviv University. He received the B.Sc (2007), M.Sc. (supervision by Prof. M. Elad and Prof. Y. C. Eldar, 2009), and Ph.D. (supervision by Prof. M. Elad 2014) degrees from the Department of Computer Science, The Technion - Israel Institute of Technology, Haifa. Raja was a postdoc at the computer science department at the Technion (Nov. 2013 till July 2014) and at the lab of Prof. G. Sapiro at Duke University, Durham, USA (July 2014 and Aug. 2015). His research interests lie at the intersection between signal and image processing and machine learning, and in particular, in deep learning, inverse problems, sparse representations, computational photography, and signal and image modeling. Raja received the EURASIP best P.hD. award, the ERC-StG grant, Maof prize for excellent young faculty (2016-2019), VATAT scholarship for excellent postdoctoral fellows (2014-2015), Intel Research and Excellence Award (2005, 2013), the Excellence in Signal Processing Award (ESPA) from Texas Instruments (2008) and was part of the Azrieli Fellows program (2010-2013). He has organized workshops and tutorials on deep learning theory in various conferences including ICML, CVPR, and ICCV. He serves as a consultant in various high-tech companies including Innoviz technologies. A computational imaging technology he has developed at Tel Aviv University together with Prof. David Mendelovic serves as the basis for the MultiVu technologies startup that he has co-founded in 2019.
Title: Joint Design of Optics and Post-Processing Algorithms Based on Deep Learning for Generating Advanced Imaging Features
Talk 1 [Date: Tuesday, May 19, 2020 ]
Laura Waller - UC Berkeley, USA
Laura Waller is the Ted Van Duzer Associate Professor of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, a Senior Fellow at the Berkeley Institute of Data Science, and affiliated with the UCB/UCSF Bioengineering Graduate Group. She received B.S., M.Eng. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT) in 2004, 2005 and 2010, and was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012. She is a Packard Fellow for Science & Engineering, Moore Foundation Data-driven Investigator, Bakar Fellow, OSA Fellow and Chan-Zuckerberg Biohub Investigator. She has recieved the Carol D. Soc Distinguished Graduate Mentoring Award, Agilent Early Career Profeessor Award (Finalist), NSF CAREER Award and the SPIE Early Career Achievement Award.
Title: End-To-End Learning for Computational Microscopy
Talk 2 [Date: Tuesday, June 2, 2020 ]
Michael Unser - Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Michael Unser (M’89–SM’94–F’99) is professor and director of EPFL's Biomedical Imaging Group, Lausanne, Switzerland. His primary area of investigation is biomedical image processing. He is internationally recognized for his research contributions to sampling theory, wavelets, the use of splines for image processing, stochastic processes, and computational bioimaging. He has published over 350 journal papers on those topics. He is the author with P. Tafti of the book “An introduction to sparse stochastic processes”, Cambridge University Press 2014. From 1985 to 1997, he was with the Biomedical Engineering and Instrumentation Program, National Institutes of Health, Bethesda USA, conducting research on bioimaging.
Dr. Unser has served on the editorial board of most of the primary journals in his field including the IEEE Transactions on Medical Imaging (associate Editor-in-Chief 2003-2005), IEEE Trans. Image Processing, Proc. of IEEE, and SIAM J. of Imaging Sciences. He is the founding chair of the technical committee on Bio Imaging and Signal Processing (BISP) of the IEEE Signal Processing Society. Prof. Unser is a fellow of the IEEE (1999), an EURASIP fellow (2009), and a member of the Swiss Academy of Engineering Sciences. He is the recipient of several international prizes including five IEEE-SPS Best Paper Awards and two Technical Achievement Awards from the IEEE (2008 SPS and EMBS 2010).
Title: CryoGAN: A novel paradigm for single-particle analysis and 3D reconstruction in cryo-EM microscopy
Talk 3 [Date: Tuesday, June 16, 2020 ]
Katherin L. (Katie) Bouman - Caltech, USA
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.
Title: Capturing the First Image of a Black Hole & Designing the Future of Black Hole Imaging
Talk 4 [Date: Tuesday, June 30, 2020]
Jong Chul Ye - KAIST, 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.
Title: Optimal transport driven CycleGAN for unsupervised learning in inverse problems
Talk 5 [Date: Tuesday, July 14, 2020]
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: Depth Estimation from RGB Images with Applications to Novel View Synthesis and Autonomous Navigation
[Date: Tuesday, July 28, 2020]
Xiao Xiang Zhu - Technische Universität München (TUM), Germany
Xiao Xiang Zhu received the M.Sc., Dr.-Ing., and Habilitation degrees in signal processing from the Technical University of Munich (TUM), Munich, Germany, in 2008, 2011, and 2013, respectively.,She is currently a Professor of signal processing in earth observation with the TUM and the German Aerospace Center (DLR), Bremen, Germany, and the Head of the Department EO Data Science, Earth Observation Center im DLR, Weßling, Germany. She is also the Head of the Helmholtz Young Investigator Group SiPEO with the DLR and TUM. She is currently co-coordinating the Munich Data Science Research School, TUM. She is also leading the research field of aeronautics, space, and transport with the Helmholtz Artificial Intelligence Cooperation Unit (HAICU). She was a Guest Scientist or a Visiting Professor with the Italian National Research Council (CNR-IREA), Naples, Italy, in 2009, Fudan University, Shanghai, China, in 2014, The University of Tokyo, Tokyo, Japan, in 2015, and the University of California at Los Angeles, Los Angeles, CA, USA, in 2016. Her main research interests include remote sensing and earth observation, signal processing, machine learning, and data science, with a special application focus on global urban mapping.,Dr. Zhu is a member of Young Academy (Junge Akademie/Junges Kolleg) with the Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany, the German National Academy of Sciences Leopoldina, Schweinfurt, Germany, and the Bavarian Academy of Sciences and Humanities, Munich, Germany. She is an Associate Editor of IEEE Transactions on Geoscience and Remote Sensing
Title: Data Science in Earth Observation
[Date: Tuesday, August 11, 2020]
Saiprasad Ravishankar - Michigan State University (MSU), 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 then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019. 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 (ISBI) 2018, and other papers were award finalists at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017 and ISBI 2020. He is currently a member of the IEEE Computational Imaging Technical Committee. He has organized special sessions or workshops on computational imaging themes at the Institute for Mathematics and its Applications (IMA), the IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop 2016, MLSP 2017, ISBI 2018, and the International Conference on Computer Vision (ICCV) 2019.
Title: From Transform Learning to Deep Learning and Beyond for Imaging
[Date: Tuesday, August 25, 2020]
Anat Levin - Technion, Israel
Anat Levin is an Associate Prof. at the department of Electrical Engineering, Technion, Israel, doing research in the field of computational imaging. She received her Ph.D. from the Hebrew University at 2006. During the years 2007–2009 she was a postdoc at MIT CSAIL, and during 2009–2016 she was an Assistant and Associate Prof. at the department of Computer Science and Applied Math, the Weizmann Inst. of Science.
Title: Rendering speckle statistics in scattering media and its applications in computational imaging
[Date: Tuesday, September 8, 2020]
Pier Luigi Dragotti - Imperial College, UK
Pier Luigi Dragotti (Fellow, IEEE) received the Laurea degree (summa cum laude) in electronic engineering from the University Federico II, Naples, Italy, in 1997, the master's degree in communications systems from the Swiss Federal Institute of Technology of Lausanne, Lausanne, Switzerland, in 1998, and the Ph.D. degree from EPFL, Switzerland, in Apr. 2002. He is a Professor of signal processing with the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He has held several visiting positions. In particular, he was a Visiting Student with Stanford University, Stanford, CA in 1996, a Summer Researcher with the Mathematics of Communications Department, Bell Labs, Lucent Technologies, Murray Hill, NJ, USA, in 2000 and a Visiting Scientist with the Massachusetts Institute of Technology in 2011. Before joining Imperial College in November 2002, he was a Senior Researcher with EPFL working on distributed signal processing for sensor networks for the Swiss National Competence Center in Research on Mobile Information and Communication Systems. His research interests include sampling theory, wavelet theory and its applications, sparsity-driven signal processing with application in image super-resolution, neuroscience and field estimation using sensor networks.,Dr. Dragotti was Technical Co-Chair for the European Signal Processing Conference in 2012, an Associate Editor for the IEEE Transactions on Image Processing from 2006 to 2009, an Elected Member of the IEEE Image, Video and Multidimensional Signal Processing Technical Committee and of the IEEE Signal Processing Theory and Methods Technical Committee. He was also the recipient of an ERC Starting Investigator Award. He is currently an Editor-in-Chief of the IEEE Transactions on Signal Processing and a member of the IEEE Computational Imaging Technical Committee.
[Talk is cancelled]
John Wright - Columbia University , USA
John Wright is an associate professor in Electrical Engineering at Columbia University. He is also affiliated with the Department of Applied Physics and Applied Mathematics and Columbia’s Data Science Institute. He received his PhD in Electrical Engineering from the University of Illinois at Urbana Champaign in 2009. Before joining Columbia he was with Microsoft Research Asia from 2009-2011. His research interests include sparse and low-dimensional models for high-dimensional data, optimization (convex and otherwise), and applications in imaging and vision. His work has received a number of awards and honors, including the 2012 COLT Best Paper Award and the 2015 PAMI TC Young Researcher Award.
Title: Geometry and Symmetry in (some!) Nonconvex Optimization Problems
[Date: Tuesday, October 6, 2020]
Bihan Wen - Nanyang Technological University, Singapore
Bihan Wen is a Nanyang Assistant Professor at Nanyang Technological University, Singapore. He received the B.Eng. degree in Electrical and Electronic Engineering from Nanyang Technological University, 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, inverse problems, and applications in image and video processing, computer vision, security, and computational imaging. Bihan Wen is an elected member of the IEEE Computational Imaging (CI) Technical Committee. He regularly serves as the conference area chairs for ICIP and ICASSP, and also co-organized many events such as the LCI workshop @ ICCV 2019 and MIPR 2019. He was the recipient of the 2016 Yee Fellowship, and the 2012 Professional Engineers Board Gold Medal in Singapore. One paper he co-authored received the Top 10% Best Paper Award at ICIP 2014, and another was awarded the Best Paper Runner-Up at the ICME 2020.
Title: From Signal Processing to Machine Learning: How "Old" Ways Can Join The New
[Date: Tuesday, October 20, 2020]
Nicole Seiberlich - University of Michigan, USA
Nicole Seiberlich received the B.S. degree in chemistry from Yale University, New Haven, CT, USA, in 2001, and the Ph.D. degree from the University of Wuerzburg, Würzburg, Germany, in 2008, with a focus on novel magnetic resonance imaging techniques.,In 2011, she became a Faculty Member of Biomedical Engineering at Case Western Reserve University, Cleveland, OH, USA, where she later became the Elmer Lincoln Lindseth Associate Professor of Biomedical Engineering, with secondary appointments in the Departments of Radiology, Cardiology, and Electrical Engineering and Computer Science. Her work in rapid MRI is funded by the NIH and NSF. She is currently an Associate Professor of radiology with the University of Michigan, Ann Arbor, MI, USA. She has authored or coauthored more than 50 peer-reviewed articles on the topics of rapid and quantitative MRI.,Dr. Seiberlich was a recipient of a number of awards for teaching and mentorship, including the CWRU Diekhoff Award for Excellence in Graduate. She has been invited to give more than 60 lectures, including the ISMRM/NIBIB New Horizons Lecture. She is a member of the Editorial Board for Magnetic Resonance in Medicine and an Associate Editor for the IEEE Transactions in Medical Imaging. She is active in multiple societies, including the International Society for Magnetic Resonance in Medicine.
Title: Bringing New Imaging Technologies to the Clinic
[Date: Tuesday, November 3, 2020]
Yoram Bresler - University of Illinois at Urbana-Champaign (UIUC), USA
Yoram Bresler received the B.Sc. (cum laude) and M.Sc. degrees from the Technion, Israel Institute of Technology, in 1974 and 1981, respectively, and the Ph.D. degree from Stanford University, Stanford, CA, USA, in 1986, all in electrical engineering. In 1987 he joined the University of Illinois at Urbana-Champaign, where he currently holds the title of a Founder Professor of Engineering with the Departments of Electrical and Computer Engineering and Bioengineering, and the Coordinated Science Laboratory. He is also President and Chief Technology Officer at InstaRecon, Inc., a startup company he cofounded to commercialize breakthrough technology for tomographic reconstruction developed in his academic research. His current research interests include machine learning and statistical signal processing and their applications to inverse problems in imaging, and in particular compressed sensing, computed tomography, and magnetic resonance imaging.
Title: Two Topics in Deep Learning for Image Reconstruction: (i) Physics-based x-ray scatter correction for CT; and (ii) Adversarial training for improved robustness
[Date: Tuesday, November 17, 2020]
Singanallur V Venkatakrishnan - Oak Ridge National Laboratory, USA
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.
Title: Pushing the Limits of Scientific CT Instruments using Algorithms : Model-based and Data-Driven Approaches
[Date: Tuesday, December 1, 2020]
J. Webster Stayman, is an Associate Professor in the Biomedical Engineering Department at Johns Hopkins University. His research interests include the design, analysis, and optimization of imaging systems including both hardware and software design.
Title: Novel data acquisition and task-based optimization in computed tomography
[Date: Tuesday, December 15, 2020]