Keynote Speakers
The ISCS features 6 world-renowned keynote speakers from light imaging, electron imaging, biomedical imaging, RADAR and remote sensing, astronomical imaging, and signal processing and deep learning.
Title: Making every Electron Count – Strategies for Sparse Data Acquisition and Processing in Electron Microscopy
Bio: Professor Angus Kirkland was awarded his MA and PhD from the University of Cambridge and has held the posts of Professor of Materials at Oxford since 2005 and JEOL Professor of Electron Microscopy since 2013. In 2016 he was appointed as Director of the National Physical Sciences Imaging Centre at Diamond Lightsource and is Science Director at the recently established Rosalind Franklin Institute.
He was awarded the MSA prize in 2005, the Rose prize in 2015, the Quadrennial prize of the European Microscopy Society in 2016 and the Agar Medal for Electron Microscopy in 2017.
He served as General Secretary of the International Federation of Societies for Microscopy in from 2014 -2018 and as President from 2018-2024. He has also served as Editor in Chief of Ultramicroscopy since 2010.
Abstract: TBA
Title: The Role of Models in Computational Sensing
Bio: Computational methods have become an integral part of array imaging systems. Modern systems work by co-designing hardware and software, using models and computation to address hardware limitations. This interplay enables trade-offs between design parameters such as hardware and algorithmic complexity, and device cost and computation time, among others. A key element in navigating these trade-offs is modeling.
In this talk we will discuss the role of modeling in acquisition systems. Modern computational methods rely on signal, system and noise models to define an inverse problem that can recover the observed signal from the acquired data. Among other things, these models can be simple or complex, they can be linear or nonlinear, they can be analytical or learned or a combination of both.
After a brief overview of the design choices, with examples from array processing and radar imaging, we will explore the latest trends in modeling, beyond linear acquisition and sparse signal models. These include purely learning-based models as well as models that combine analytical knowledge and learned components, such as physics informed neural networks. We will show how these models can significantly improve performance in radar imaging, especially in the presence of multiple scattering and other disturbances. We will also explore the computational and complexity trade-offs for practical use and conclude with some lessons learned and some guidelines.
Abstract: Petros T. Boufounos is a Distinguished Research Scientist, a Deputy Director and the Computational Sensing Senior Team Leader at Mitsubishi Electric Research Laboratories (MERL). Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between September 2006 and December 2008, he was a postdoctoral associate with the Digital Signal Processing Group at Rice University. Dr. Boufounos joined MERL in January 2009, where he has been heading the Computational Sensing Team since 2016.
Dr. Boufounos' immediate research focus includes signal acquisition and processing, computational sensing, inverse problems, quantization, and data representations. He is also interested in how signal acquisition interacts with other fields that use sensing extensively, such as machine learning, robotics, and dynamical system theory. He has over 40 patents granted and more than 10 pending, and more than 100 peer reviewed journal and conference publications in these topics. Dr. Boufounos was the general co-chair of the ICASSP 2023 organizing committee and a regional director-at-large in the IEEE Signal Processing Society's Board of Governors. He has also served as an Area Editor and a Senior Area Editor for the IEEE Signal Processing Letters, an AE for IEEE Transactions on Computational Imaging, and as a member of the SigPort editorial board and the IEEE Signal Processing Society Theory and Methods technical committee. Dr. Boufounos is an IEEE Fellow and an IEEE SPS Distinguished Lecturer for 2019-2020.
Title: Self-Supervised Machine Imaging
Bio: Mike Davies holds the Jeffrey Collins Chair in Signal and Image Processing at the University of Edinburgh where he is also Director of Research for the School of Engineering. He is a leading expert in the mathematical theory and algorithm design for computational imaging, compressed sensing, and machine learning systems. From 2013-24 he led the University Defence Research Collaboration (UDRC) a major UK research programme for signal processing in defence in collaboration with Dstl. With over 300 publications and over 19,000 citations, his research has been recognised through numerous keynote and invited talks, 5 ESI highly cited papers, 4 paper awards, and underpins leading commercial imaging and sensing systems. He has served on various professional committees and consulted for both UK government and industry on signal processing and AI. He has been the recipient of various awards and prizes including a Royal Society University Research Fellowship, a prestigious ERC advanced grant, and a Royal Society Wolfson Research Merit Award. He is a Fellow of IEEE, the European Society for Signal Processing (EURASIP), the Royal Society of Edinburgh, and the Royal Academy of Engineering.
Abstract: Today modern deep learning methods provide the state-of-the-art in image reconstruction in most area of computational imaging. However, such techniques are very data hungry and in a number of key imaging problems access to ground truth data is challenging if not impossible. This has led to the emergence of a range of self-supervised learning algorithms for imaging that attempt to learn the imaging solution without ground truth data. In this talk I will review some of the existing techniques and look at what is and might be possible in self-supervised imaging.
Title: The Imaging Of The Supermassive Black Hole Shadows With EHT
Bio: Shiro Ikeda graduated and received PhD from the University of Tokyo in 1991 and 1996, respectively. From 1996 to 2001, he was a postdoctoral researcher at the RIKEN Brain Science Institute. He then joined the Kyushu Institute of Technology as an associate professor. Since 2003, he has been a faculty member at the Institute of Statistical Mathematics, where he is currently a professor. He is also a visiting scholar at the National Astronomical Observatory of Japan and the Kavli Institute for the Physics and Mathematics of the Universe. He has been working in the field of applied mathematics, which includes but is not limited to statistical signal processing, information theory, information geometry, statistics, and astrostatistics.
Abstract: The Event Horizon Telescope (EHT) is a global network of radio telescopes that forms a virtual Earth-sized radio interferometric telescope. The EHT Collaboration (EHTC) comprises over 300 members from diverse backgrounds and countries and the primary goal is to capture images of the supermassive black holes. In April 2019 and May 2022, the EHTC released the black hole shadow images of M87 and our Milky Way galaxy, respectively. The EHT observation took place in April 2017 and needed 2 and 5 years for image processing of each one, respectively. We have developed novel imaging methods to reconstruct the images and to address important questions of physics. In this presentation, I will describe the methodologies we have developed and explain how we established our claim.
Title: Imaging with Scattered Light
Bio: Sylvain Gigan is Professor of Physics at Sorbonne Université in Paris, and group leader in Laboratoire Kastler-Brossel, at Ecole Normale Supérieure (ENS, Paris). His research interests focuses on light propagation in complex media, and range from fundamental investigations, biomedical imaging, computational imaging, signal processing, to quantum information. He is also the cofounder of a spin-off: LightOn, developing optical computing solutions for machine learning.
After graduating from Ecole Polytechnique (Palaiseau France) in 2000, and a Master Specialization in Optics from University Paris XI (Orsay, France), he obtained a PhD in Physics 2004 from University Pierre and Marie Curie (Paris, France) in quantum and non-linear Optics. From 2004 to 2007, he was a postdoctoral researcher in Vienna University (Austria), working on quantum optomechanics, in the group of Markus Aspelmeyer and Anton Zeilinger. from 2007 to 2014, he was at ESPCI ParisTech in Paris, as Associate Professor, and started working on optical imaging in complex media and wavefront shaping techniques, at the Langevin Institute. He joined Sorbonne Université as Professor in 2014. He was awarded the Fabry de Gramont Prize of the French Optical Society in 2016, The Joseph Fourier ATOS prize in 2018, the Jean Jerphagnon Prize in 2019. He is the recipient of two ERC grants in 2011 and 2017. He is Optica Fellow, Junior and Senior the Institut Universitaire de France (2016-2021 and 2023-2028). He is Editor of Optica, Intelligent Computing & eLight. He is editor-in-chief of Advanced Imaging.
Abstract: Imaging in scattering media, such as biological tissues, is an extremely challenging problem. Due to the exponential attenuation of ballistic light, and to the seemingly random multiple scattering process. Wavefront shaping has revolutionized this paradim : it allows focusing and imaging in disordered media, exploiting the ability to control multiply scattered light. Many so-called guide-star mechanisms have been investigated to deliver light and image non-invasively at depth. However, the most common microscopy contrast mechanism, fluorescence, is incoherent and remains extremely challenging. I will discuss some of our recent works, exploiting signal processing and machine learning frameworks, to recover such incoherent images behind scattering layers.
Title: Computational 3D Imaging Through hair-thin Optical Fibers
Bio: Lyuba Amitonova is an assistant professor at Vrije University Amsterdam and leads a research group at the Advanced Research Center for Nanolithography (ARCNL) in the Netherlands. She is an expert in computational optical imaging, fiber endoscopy, and advanced wavefront shaping. She obtained her PhD from the Lomonosov Moscow State University (Russia) in 2013 and subsequently worked as a postdoc at the University of Twente (Netherlands). Lyuba is a recipient of several prestigious personal grants from the Dutch Research Council (NWO). The focus of her current work is on the development of new imaging methods for neuroscience and the semiconductor industry.
Abstract: Advances in imaging tools have always been a key driver for new discoveries in many different research fields including life science, medicine, and nanolithography. Developing new approaches to visualize sub-cellular structures in hard-to-reach locations, e.g. deep brain tissues of a freely moving mouse, is essential to advance our knowledge. I will present the new methods of computational 3D fluorescence imaging and quantitative phase imaging with super-resolution through an ultimately thin and flexible optical fibers.