Katie Bouman
Dr. Katherine L. (Katie) Bouman is an assistant professor in the Computing and Mathematical Sciences, Electrical Engineering, and Astronomy Departments at the California Institute of Technology. Her work combines ideas from signal processing, computer vision, machine learning, and physics to find and exploit hidden signals for scientific discovery. 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, and her bachelor’s degree in Electrical Engineering from the University of Michigan. She is a Rosenberg Scholar, Heritage Medical Research Institute Investigator, recipient of the Royal Photographic Society Progress Medal, Electronic Imaging Scientist of the Year Award, University of Michigan Outstanding Recent Alumni Award, and co-recipient of the Breakthrough Prize in Fundamental Physics. As part of the Event Horizon Telescope Collaboration, she is co-lead of the Imaging Working Group and acted as coordinator for papers concerning the first imaging of the M87* and Sagittarius A* black holes.
YUEJIE chi
Dr. Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with courtesy appointments in the Machine Learning department and CyLab. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, decision making, and AI systems. Among others, Dr. Chi received the Presidential Early Career Award for Scientists and Engineers (PECASE), SIAM Activity Group on Imaging Science Best Paper Prize, IEEE Signal Processing Society Young Author Best Paper Award, and the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.
Miguel Heredia Conde
Miguel Heredia Conde received the Dr.Eng. degree in sensor signal processing from the University of Siegen, Siegen, Germany, in 2016 and the Habilitation degree from the same university in 2022. In 2013, he joined the Center for Sensor Systems (ZESS), University of Siegen. Since then, he has also been a member of the DFG Research Training Group Graduiertenkolleg (GRK) 1564 “Imaging New Modalities.” Since 2016, he has been the Leader of the research group “Compressive Sensing for the Photonic Mixer Device” at ZESS, and since 2020, he has also been the General Manager of the H2020-Marie Skłodowska-Curie Innovative Training Network (MSCA-ITN) MENELAOSNT. In 2023 he joined the Institute for High-Frequency and Communication Technology, University of Wuppertal, where he is the Head of the research group on "Computational 3D Imaging". His current research interests include time-of-flight imaging systems, such as those based on the photonic mixer device (PMD), Terahertz imaging, compressive sensing, computational imaging, and unconventional sensing.He has been responsible for two lectures with focus on Compressive Sensing (CS) at the University of Siegen from 2017 to 2023 and another two at the University of Wuppertal from 2024 with focus on CS and optical imaging and sensing, respectively. Dr. Heredia Conde is a member of the IEEE Signal Processing Society (SPS) and the IEEE Standards Association (SA) and a regular reviewer of top-level conferences (ICASSP, etc.), multiple Elsevier and IEEE Transactions, Letters, and Journals, and of the DFG (German Research Foundation). He has been a visiting researcher at CiTIUS (Area of Artificial Vision), University of Santiago de Compostela, at the Faculty of Physics (Division of Information Optics), University of Warsaw, and at the Department of Electrical and Electronic Engineering, Imperial College London. In 2020 he has been a visiting lecturer at the Department of Applied Mathematics, University of Vigo. He has also been an invited speaker at multiple conferences and seminars. Dr. Conde is the Chair of the P3382 Performance Metrics for Magnetic Resonance Image (MRI) Reconstruction Working Group of the IEEE Synthetic Aperture Standards Committee.Dr. Heredia Conde was one of the recipients of the 2006 Academic Excellence Prices, awarded by the Government of Galicia, Spain. In 2017, he received the University of Siegen Prize for International Young Academics. In 2020 his collaborative work with Prof. Bhandari (ICL) was awarded the Best Paper Award at ICCP. In 2024 his group’s work on passive ToF imaging was awarded the Best Demo Award at CoSeRa.
Anne gelb
Dr. Anne Gelb is the John G. Kemeny Parents Professor of Mathematics at Dartmouth College. Her work focuses on high order methods for signal and image restoration, classification, and change detection for real and complex signals using temporal sequences of collected data. There are a wide variety of applications for her research including speech recognition, medical monitoring, credit card fraud detection, automated target recognition, and video surveillance. A common assumption made in these applications is that the underlying signal or image is sparse in some domain. While detecting such changes from direct data (e.g. images already formed) has been well studied, Professor Gelb’s focus is on applications such as magnetic resonance imaging (MRI), ultrasound, and synthetic aperture radar (SAR), where the temporal sequence of data are acquired indirectly. In particular, Professor Gelb develops algorithms that retain critical information for identification, such as edges, that is stored in the indirect data. Professor Gelb is currently investigating how to use these techniques in a Bayesian setting so that the uncertainty of the solutions may also be quantified, and is interested in applying these techniques for purposes of sensing, modeling, and data assimilation for sea ice prediction. Her research is funded in part by the Air Force Office of Scientific Research, the Office of Naval Research, the National Science Foundation, and the National Institutes of Health, and she regularly collaborates with scientists at the Wright-Patterson Air Force Research Lab and the Cold Regions Research and Engineering Laboratory (CRREL).
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ROARKE HORSTMEYER
Roarke Horstmeyer is an assistant professor of Biomedical Engineering and Electrical and Computer Engineering at Duke University. He is also the Scientific Director at Ramona Optics. He develops microscopes, cameras and computer algorithms for a wide range of applications, from forming large-area, high-resolution 3D videos of freely moving organisms to detecting blood flow and brain activity deep within tissue. Dr. Horstmeyer’s lab currently performs research within the fields of ptychography, high-content microscopic imaging, physics informed machine learning algorithms, and biophotonic measurement systems. Before joining Duke in 2018, Dr. Horstmeyer was a visiting professor at the University of Erlangen in Germany and an Einstein International Postdoctoral Fellow at Charité Medical School in Berlin. Prior to his time in Germany, Dr. Horstmeyer earned a PhD from Caltech’s Electrical Engineering department (2016), an MS from the MIT Media Lab (2011), and bachelor’s degrees in Physics and Japanese from Duke in 2006.
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Ivo Ihrke
Ivo Ihrke is professor of Computational Sensing at University of Siegen, Germany, a member of the university's ZESS (center for sensor systems) as well as affiliated with the Fraunhofer Institute for High Frequency Physics and Radar Techniques. Prior to joining Siegen, he was a staff scientist at the Carl Zeiss research department, which he joined on-leave from Inria Bordeaux Sud-Ouest, where he was a permanent researcher. At Inria he led the research project "Generalized Image Acquisition and Analysis" which was supported by an Emmy-Noether fellowship of the German Research Foundation (DFG). Prior to that he was heading a research group within the Cluster of Excellence "Multimodal Computing and Interaction" at Saarland University. He was an Associate Senior Researcher at the MPI Informatik, and associated with the Max-Planck Center for Visual Computing and Communications. Before joining Saarland University he was a postdoctoral research fellow at the University of British Columbia, Vancouver, Canada, supported by the Alexander von Humboldt-Foundation. He received a MS degree in Scientific Computing from the Royal Institute of Technology (KTH), Stockholm, Sweden and a PhD (summa cum laude) in Computer Science from Saarland University.
Ivo has been the organizer of several Computational Imaging events, such as the CVPR Workshop PROCAMS in 2012, the GCPR Workshop on Imaging New Modalities in 2013, a Dagstuhl seminar on Computational Imaging in 2015, the ZEISS Symposium on Computational Imaging in 2016 and a Heraeus Seminar on Computational Optical Microscopy in 2026. He has been program chair of the German Conference on Pattern Recognition (GCPR) in 2022 and will be general chair of Vision, Modeling and Visualization (VMV) in 2026.
He holds 20+ patents and is a cofounder of K|Lens GmbH, a company specializing in plenoptic imaging for industrial quality control and artificial intelligence. He is interested in all aspects of Computational Imaging, including theory, mathematical modeling, algorithm design and their efficient implementation, as well as hardware concepts and their experimental realization and characterization.
He holds 20+ patents and is a cofounder of K|Lens GmbH, a company specializing in plenoptic imaging for industrial quality control and artificial intelligence. He is interested in all aspects of Computational Imaging, including theory, mathematical modeling, algorithm design and their efficient implementation, as well as hardware concepts and their experimental realization and characterization.
david lindell
David Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto. Prior to joining the University of Toronto, he received his Ph.D. from Stanford University. His work combines emerging sensors, machine learning, and physics-based models to enable new capabilities in visual computing. He is a recipient of the ACM SIGGRAPH Outstanding Dissertation Award Honorable Mention, a Google Research Scholar award, a Sony Faculty Innovation Award, and the 2023 Marr Prize.
LEI TIAN
Lei Tian is an Associate Professor in the Department of Electrical and Computer Engineering and Biomedical Engineering, and directs the Computational Imaging Systems Lab at Boston University (BU). He received his Ph.D. from MIT and was a postdoctoral associate at UC Berkeley. His research focuses on developing computational imaging techniques for biomedical and semiconductor applications. Dr. Tian is a Scialog Fellow in Advancing BioImaging and was awarded the NSF CAREER Award and the BU College of Engineering Early Career Excellence in Research Award.
Dr. Xueying Yu is a junior faculty at the Atmospheric Sciences Research Center, the State University of New York at Albany. Her work focuses on greenhouse gases and air pollutants, to address critical knowledge gaps related to land-atmosphere-ocean exchange, climate-chemistry interactions, and their changes over time. Previously, Dr. Yu was a postdoctoral fellow in Earth System Science at the Jackson Lab, Stanford University. Dr. Yu received the PhD degree from University of Minnesota Twin Cities in 2022, as a future investigator of the NASA Earth and Space Science Fellowship, advised by Prof. Dylan Millet. She received the Bachelor degree in Atmospheric Science and a Master degree in Meteorology from Nanjing University.
