alin achim, university of bristol, united kingdom
biography
Professor Alin Achim received the B.Sc. and M.Sc. degrees in electrical engineering from “Politechnica” University of Bucharest, Romania, in 1995 and 1996, respectively, and the Ph.D. degree in biomedical engineering from the University of Patras, Greece, in 2003. He then obtained an European Research Consortium for Informatics and Mathematics (ERCIM) Post-doctoral Fellowship, which he spent with the Institute of Information Science and Technologies (ISTI-CNR), Pisa, Italy, and the French National Institute for Research in Computer Science and Control (INRIA) Sophia Antipolis, France. In October 2004, he joined the Department of Electrical and Electronic Engineering, University of Bristol, Bristol, U.K., as a Lecturer, where he became a Senior Lecturer (Associate Professor) in 2010 and a Reader in biomedical image computing in 2015. Since August 2018, he holds the Chair of Computational Imaging, at the University of Bristol. From 2019 to 2020, he was a Leverhulme Trust Research Fellow with the Laboratoire I3S, Université Cote d’Azur. He was awarded a Chair of Excellence by the University of the Code d’Azur in 2020.
Alin has coauthored over 200 scientific publications, including 69 journal articles. His research interests include statistical signal, image, and video processing and machine learning, with applications in both biomedical imaging and Earth Observation. He was/is an Elected Member of the Bio Imaging and Signal Processing Technical Committee of the IEEE Signal Processing Society, an Affiliated Member (invited) of the Signal Processing Theory and Methods Technical Committee, and a member of the IEEE Geoscience and Remote Sensing Society’s Image Analysis and Data Fusion Technical Committee. He was/is an Associate Editor / Senior Area Editor of the IEEE Transactions on Image Processing, and of the IEEE Transactions on Computational Imaging.
Alin has coauthored over 200 scientific publications, including 69 journal articles. His research interests include statistical signal, image, and video processing and machine learning, with applications in both biomedical imaging and Earth Observation. He was/is an Elected Member of the Bio Imaging and Signal Processing Technical Committee of the IEEE Signal Processing Society, an Affiliated Member (invited) of the Signal Processing Theory and Methods Technical Committee, and a member of the IEEE Geoscience and Remote Sensing Society’s Image Analysis and Data Fusion Technical Committee. He was/is an Associate Editor / Senior Area Editor of the IEEE Transactions on Image Processing, and of the IEEE Transactions on Computational Imaging.
Computational Synthetic Aperture Radar Imaging with Model-Based Machine Learning
Synthetic Aperture Radar (SAR) imaging is increasingly constrained by data acquisition costs, bandwidth, and onboard processing limitations, motivating the development of compressive sensing approaches. However, conventional reconstruction methods applied to sub-sampled radar data often suffer from severe artefacts, including aliasing and loss of resolution. This talk presents a novel computational imaging framework that integrates modern generative machine learning with principled model-based methods to address these challenges.At its core, the approach leverages Denoising Diffusion Probabilistic Models (DDPMs) to perform high-quality image reconstruction from heavily sub-sampled SAR measurements. These data-driven models are further enhanced through the incorporation of statistical priors and inverse problem formulations, reflecting the heavy-tailed nature and sparsity of SAR imagery. In particular, we introduce a class of hybrid algorithms in which model-based optimisation steps, such as Forward-Backward Splitting and Cauchy Proximal Splitting, are interleaved with the diffusion process, enabling explicit enforcement of physically meaningful constraints without retraining the generative model.
We further explore deep-unfolded variants of these optimisation schemes, combining interpretability with computational efficiency and adaptability. Experimental results on real SAR datasets demonstrate that these model-based machine learning approaches significantly improve reconstruction quality, even under aggressive sub-sampling regimes. The presented framework highlights a promising pathway towards efficient, high-fidelity SAR imaging systems suitable for next-generation onboard and resource-constrained platforms.
ADRIANO CAMPS, Universitat Politècnica de Catalunya, SPAIN
BIOGRAPHY
Adriano Camps joined the Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), as an Assistant Professor in 1993, Associate Professor in 1997, and Full Professor since 2007. In 1999, he was on sabbatical leave at the Microwave Remote Sensing Lab., of the Univ. of Massachusetts, Amherst. Since September 2022 he has been an ASPIRE Visiting International Professor at the UAE University, Al Ain, Abu Dhabi.
His research interests are focused on: 1) microwave remote sensing, with special emphasis on microwave radiometry by aperture synthesis (Ph.D. Thesis about the MIRAS instrument, which became the single payload of ESA’s SMOS mission), 2) remote sensing using signals of opportunity (GNSS-R), 3) radio frequency interference detection and mitigation, 4) ionospheric propagation, and 5) nanosatellites as a tool to test innovative remote sensors.
His publication record includes over 268 papers in peer-reviewed journals, 9 book chapters, and the book Emery and Camps, “Introduction to Satellite Remote Sensing. Atmosphere, Ocean, Land and Cryosphere Applications,” Elsevier, 2017, 860 pages), and more than 541 conference presentations. According to Google Scholar/Scopus, his h-index is 63/48, and his publications have received more than 15.665/10.817 citations. According to the October 2023 Stanford ranking, he is among the top 2% of researchers in all categories.
Prof. Camps holds 12 patents and has advised 30 Ph. D. Thesis students (+ 10 ongoing), and more than 150 B.Eng. final degree and M.Eng. Theses. These Ph.D. students have now responsibility positions at universities, companies, and research centers, including NASA/JPL, ESA, and Airbus, and two have started their own companies with Prof. Camps’ participation, having transferred a total of five patents to these.
Prof. Camps was the Scientific Coordinator of the CommSensLab Research Center (María de Maeztu Excellence Research Unit 2016-2020) at the Dept of Signal Theory and Communications, UPC. Within CommSensLab, he co-led the Remote Sensing Lab (prs.upc.edu/ ), and leads the UPC NanoSat Lab (nanosatlab.upc.edu/en ). He is the PI of the first four UPC nano-satellites: 1) Cat-1: 1U CubeSat with 7 tech demos, 2) Cat-2, a 6U CubeSat with an innovative dual-frequency dual-polarization GNSS-R payload, 3) Cat-4, a 1U Cubesat with an SDR implementing a microwave radiometer, a GNSS-Reflectomer, and AIS receiver, 4) FSSCAT, a tandem mission formed by two 6U CubeSats (Cat-5/A and /B), and 5) the IEEE/GRSS Open PocketQube Kit (Cat-1, -2, -3). FFSCAT was the winner of the 2017 Copernicus Masters Competition, and it is the first mission contributing to the Copernicus System based on CubeSats. FSSCAT was produced for the first time using CubeSats, scientific quality soil moisture, sea ice extent, concentration and thickness, and sea salinity maps in the Arctic.
Prof. Camps was Chair of uCal 2001, Technical Program Committee (TPC) Co-chair of IGARSS 2007, co-chair of GNSS-R ’10, general co-chair of IGARSS 2020, co-chair of the 6th Fractionated and Federated Satellite Systems Workshop, member of the organizing committee of the ESA 4th Symposium on Space Educational Activities (SSEA), and has participated in all TPCs of the International Geoscience and Remote Sensing Symposium (IGARSS) since 2000, and in the TPCs of other conferences such as MicroRad, M2GARSS, Congreso de la AET, Asamblea Nacional de la URSI (Spain) etc.
Prof. Camps was Associate Editor of Radio Science, IEEE Geoscience and Remote Sensing Letters, and IEEE Transactions on Geoscience and Remote Sensing, and has been guest editor of several special issues in IEEE and MDPI. He was the President-Founder of the IEEE Geoscience and Remote Sensing Society (GRSS) Chapter in Spain, he is the Counselor of the IEEE Student Branch at UPC-BarcelonaTech, and in 2017-2018 he was the President of the IEEE Geoscience and Remote Sensing Society.
Prof. Camps has received several awards for his contributions to:• Research: 1) 2nd National Award of University Studies (1993); 2) INDRA award of the COIT to the best PhD in Remote Sensing (1997); 3) UPC extraordinary Ph.D. Award (1999); 4) Research Distinction of the Generalitat de Catalunya (2002); 5) the European Young Investigator Award (2004), 6) the ICREA Academia award (2009, 2015), and 7) the elevation to the grade of Fellow of the IEEE (2011).• Technology transfer: As a member of the Microwave Radiometry Group, he received 1) the 1st Duran Farell Award (2000) 2) the Ciutat de Barcelona award (2001) for Technology Transfer, and 3) the “Salvà i Campillo” Award of the COETC for the most innovative research project for MIRAS/SMOS activities (2004), and 4) the 7th Duran Farell award for Technological Research for the work on GNSS-R instrumentation and applications (2010), 5) the 13th Duran Farell award for Technological Research for the work on the FSSCAT mission (2022), the 6) with Dr. Querol, the ESNC Award-Barcelona Challenge for the FENIX system to detect and mitigate RFI in GNSS receivers (2015), and 7) the 2017 ESA Sentinel Small Satellite Challenge and the Overall Winner of 2017 Copernicus Masters Competition.• Education: collective awards together with Profs. R. Bragós, E. Alarcón, E. Sayrol, A. Oliveras, and J. Pegueroles: 1) Jaume Vicens Vives award 2012 (17/9/2012) from the Generalitat de Catalunya for the Project “Concepció, Disseny, Implementació i Operació de l’itinerari d’assignatures de projectes d’acord amb la iniciativa International CDIO” at Telecom Barcelona, and 2) UPC award to the Teaching Quality at the University 2012 from the Social Council of the Universitat Politècnica de Catalunya, and individual award: 3) IEEE Geoscience and Remote Sensing Society – Education Award 2021
Synthetic Aperture Imaging in Microwave Radiometry: A Journey Toward New Passive Imagers
Some ideas in science and engineering are so powerful that they reappear across decades in new forms, each enabled by a new generation of technology. Synthetic aperture imaging in microwave radiometry is one of those ideas. What began with the interferometric intuition behind sea interferometry and the Mills Cross telescope evolved through the pioneering contributions of Mel’nik and Komiyama, and later through the ESTAR demonstrator, which brought thinned-array radiometry into geophysical remote sensing. This trajectory continued with the development and flight success of ESA’s MIRAS instrument onboard SMOS, the Doppler-radiometer concept as a bridge toward motion-assisted passive imaging, and more recent airborne and motion-extended implementations.
This keynote will revisit that journey, from its earliest conceptual foundations to its first practical demonstrations, and ultimately to the spaceborne maturity of ESA’s MIRAS instrument on the SMOS mission. Drawing on my own experience with MIRAS, I will discuss how synthetic aperture radiometry evolved from an intellectually compelling but highly demanding concept into a fully operational Earth observation system capable of delivering global L-band measurements of soil moisture and ocean salinity. That transition required far more than hardware innovation: it demanded new solutions in calibration, interferometric stability, image reconstruction, polarimetric accuracy, and radio-frequency interference mitigation.
Yet, the true significance of this field extends well beyond any single instrument. Synthetic aperture microwave radiometry has shown that passive imaging can be reimagined as a synthesis of sparse apertures, platform motion, signal processing, and physical insight. In that sense, MIRAS was not the endpoint of a technological story, but a decisive step in the emergence of a broader paradigm. As new concepts continue to appear, the field is moving toward lighter, more flexible, and more capable microwave observing systems. This talk will argue that synthetic aperture radiometry is not merely a mature technique, but a lasting framework for innovation—one that continues to reshape how we observe the Earth, and may ultimately expand how we explore other worlds through passive microwave sensing.
This keynote will revisit that journey, from its earliest conceptual foundations to its first practical demonstrations, and ultimately to the spaceborne maturity of ESA’s MIRAS instrument on the SMOS mission. Drawing on my own experience with MIRAS, I will discuss how synthetic aperture radiometry evolved from an intellectually compelling but highly demanding concept into a fully operational Earth observation system capable of delivering global L-band measurements of soil moisture and ocean salinity. That transition required far more than hardware innovation: it demanded new solutions in calibration, interferometric stability, image reconstruction, polarimetric accuracy, and radio-frequency interference mitigation.
Yet, the true significance of this field extends well beyond any single instrument. Synthetic aperture microwave radiometry has shown that passive imaging can be reimagined as a synthesis of sparse apertures, platform motion, signal processing, and physical insight. In that sense, MIRAS was not the endpoint of a technological story, but a decisive step in the emergence of a broader paradigm. As new concepts continue to appear, the field is moving toward lighter, more flexible, and more capable microwave observing systems. This talk will argue that synthetic aperture radiometry is not merely a mature technique, but a lasting framework for innovation—one that continues to reshape how we observe the Earth, and may ultimately expand how we explore other worlds through passive microwave sensing.
mujdat cetin, university of rochester
BIOGRAPHY
Mujdat Cetin is a Professor of Electrical and Computer Engineering and of Computer Science, and the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Artificial Intelligence at the University of Rochester. He is also serving as the Director of the New York State Center of Excellence in Data Science and Artificial Intelligence. Previously he served as a faculty member at Sabanci University, Istanbul, Turkey, and as a Research Scientist at MIT. He also held visiting faculty positions at MIT, Northeastern University, and Boston University. He received his PhD from Boston University.
Mujdat Cetin’s research interests are within the area of data, signal, and imaging sciences, and include computational imaging, bioimage analysis, and brain-computer interfaces. Mujdat Cetin served as the Editor-in-Chief of the IEEE Transactions on Computational Imaging, as the Chair of the IEEE Computational Imaging Technical Committee, and as the Technical Program Co-chair for five conferences. He received several awards including the IEEE Signal Processing Society Best Paper Award; the EURASIP/Elsevier Signal Processing Best Paper Award; the IET Radar, Sonar and Navigation Premium Award; and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is a Fellow of IEEE.
Mujdat Cetin’s research interests are within the area of data, signal, and imaging sciences, and include computational imaging, bioimage analysis, and brain-computer interfaces. Mujdat Cetin served as the Editor-in-Chief of the IEEE Transactions on Computational Imaging, as the Chair of the IEEE Computational Imaging Technical Committee, and as the Technical Program Co-chair for five conferences. He received several awards including the IEEE Signal Processing Society Best Paper Award; the EURASIP/Elsevier Signal Processing Best Paper Award; the IET Radar, Sonar and Navigation Premium Award; and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is a Fellow of IEEE.
Toward Trustworthy Learning-based Computational Imaging: A Case for Uncertainty Quantification
Modern machine learning has reshaped computational imaging, replacing handcrafted priors and model-based reconstruction with learned models trained on curated datasets. While these approaches have enabled impressive performance, they raise a fundamental question: how much should we trust their outputs? In this talk, I argue that explicit uncertainty quantification is essential for trustworthy imaging. More concretely, I take the position that a system that cannot represent its uncertainty cannot be considered reliable. I will present recent work that integrates uncertainty estimation into physics-informed, learning-based imaging frameworks by leveraging advances in generative modeling and Bayesian neural networks. The methods I present capture both aleatoric (data) and epistemic (model) uncertainty, and provide calibrated measures of confidence. Across applications including magnetic resonance imaging, computed tomography, X-ray ptychography, synthetic aperture radar, and computational photography, I will show how uncertainty estimates can (i) expose limitations in training data, (ii) detect out-of-distribution or abnormal measurements, and (iii) assess measurement quality in a statistically meaningful way. Finally, I will discuss how calibration techniques such as conformal prediction enable rigorous, distribution-free guarantees, and outline a path toward uncertainty-aware imaging systems that are not only accurate, but dependable in practice.
Michele Crosetto, Centre Tecnològic de Telecomunicacions de Catalunya, spain
biography
Michele Crosetto holds a civil engineering degree from the Politecnico di Torino (1993) and a doctorate in Topographic and Geodesic Sciences from the Politecnico di Milano (1998). He has formed part of the Institute of Geomatics since 2002. Since January 2014 he is with CTTC, Spain, where now he is head of the Geomatics Division. His main research activity is related to the analysis of spaceborne, airborne and ground-based remote sensing data and the development of scientific and technical applications using active sensor types. He is the coordinator of the Advisory Board of the European Ground Motion Service.
Contribution of SAR Interferometry to climate change monitoring
Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation. It relies on sensors to detect and measure electromagnetic radiation that is either reflected or emitted from the Earth's surface. Remote sensing can be performed using a variety of systems, which can be classified in (i) terrestrial or ground-based close-range; (ii) aerial remote sensing, which involves sensors mounted on aircraft, helicopters, or drones; and (iii) satellite remote sensing. By enabling the precise measurement of critical Earth system parameters across vast spatial and temporal scales, satellite remote sensing provides the primary data framework for climate modelling and analysis.
According to the GCOS (Global Climate Observing System), there are currently 55 Essential Climate Variables (ECVs) that describe the climate change. Out of these 55 variables, approximately 60% are directly observable or significantly supported by satellite Earth Observation. In this talk I explore the potential of Interferometric Synthetic Aperture Radar (InSAR) for climate change studies. InSAR is a passive remote sensing technique used for generating digital elevation models and measuring land deformation. We focus here on the latter application. From the climate change viewpoint, the most important InSAR initiatives are those that cover wide areas. The most emblematic initiative is the European Ground Motion Service (EGMS), part of Copernicus Land Service. Operating at European continental scale, EGMS has the potential to expand globally in the future. Concerning the contribution of InSAR to climate change monitoring, we have identified the following main InSAR applications:• Permafrost,• Glaciers,• Relative Sea Level Rise,• Aquifer monitoring,• Landslide monitoring.
According to the GCOS (Global Climate Observing System), there are currently 55 Essential Climate Variables (ECVs) that describe the climate change. Out of these 55 variables, approximately 60% are directly observable or significantly supported by satellite Earth Observation. In this talk I explore the potential of Interferometric Synthetic Aperture Radar (InSAR) for climate change studies. InSAR is a passive remote sensing technique used for generating digital elevation models and measuring land deformation. We focus here on the latter application. From the climate change viewpoint, the most important InSAR initiatives are those that cover wide areas. The most emblematic initiative is the European Ground Motion Service (EGMS), part of Copernicus Land Service. Operating at European continental scale, EGMS has the potential to expand globally in the future. Concerning the contribution of InSAR to climate change monitoring, we have identified the following main InSAR applications:• Permafrost,• Glaciers,• Relative Sea Level Rise,• Aquifer monitoring,• Landslide monitoring.
mariya doneva, Philips Innovative Technologies, germany
biography
Dr. Mariya Doneva is a Senior Scientist at Philips Innovative Technologies, Hamburg, Germany, which she joined in 2010. She received her BSc and MSc degrees in Physics from the University of Oldenburg in 2006 and 2007, respectively and her PhD degree in Physics from the University of Lübeck in 2010. Her work has yielded many innovations related to imaging workflow improvements, novel quantitative MRI approaches, and most prominently fast MRI data acquisition based on compressed sensing and more recently deep learning allowing significant reduction of the scan time of routine clinical scans, which has been integrated in the clinical routine of many hospitals.
New Frontiers in MRI image formation: Towards Faster, More Robust, and More Quantitative Imaging
Healthcare systems worldwide face increasing pressure from aging populations, staff shortages, and the growing demand for personalized medicine. While MRI demand rises, it remains an expensive modality with limited availability—particularly in low‑ and middle‑income countries. Environmental considerations, including energy consumption and helium dependency, add further constraints.
These global challenges are shaping the next wave of innovation in Magnetic Resonance Imaging. This presentation highlights emerging developments that fundamentally rethink how MRI data are acquired, reconstructed, and interpreted. The recent innovations span hardware, workflow optimization, image formation, and advanced data analysis. The main focus of this presentation will be on image formation, with connections made to technological advances in the other areas, where relevant.
For the purpose of this presentation, I will consider how MR image formation techniques can address three central goals: making MRI faster, more robust to artifacts, and providing more quantitative and reproducible measurements that support precision medicine.
Faster scanningI will begin by discussing advances in rapid and undersampled data‑acquisition strategies—ranging from parallel imaging to compressed sensing and modern AI‑based reconstruction methods—that significantly reduce scan times while improving clinical efficiency. These approaches are closely linked to the growing field of image enhancement, including deep‑learning‑based denoising and sharpening.The recent resurgence of interest in low‑ and ultra‑low‑field MRI, driven by accessibility and portability needs, has been fueled by these reconstruction advancements, enabling clinically useful image quality despite hardware constraints.
More robust MRINoise is not the only issue that can affect the MRI image quality. Image artifacts are common due to simplifications in the data modelling but also due to external factors like patient motion. Many of these artifacts have been accepted as the price that needs to be paid for faster scanning. Some improvements can be achieved with hardware development. Others have found fixes in data acquisition techniques. Often image artifacts can be reduced or even removed by using a more accurate signal model, however this comes at the expense of more complex reconstruction and sometimes additional measurements. With the reduction in computation cost and advancements in computational imaging we also see more approached for artifact reduction leasing to more robustness and improved diagnostic quality.
Quantitative MRIFinally, I will discuss the growing movement toward quantitative MRI (qMRI), which aims to extract reproducible, objective biomarkers instead of qualitative image contrast. Both the developments of faster scanning and more accurate signal modelling have impacted quantitative MRI techniques. While the development is slow we do see constant improvements in quantitative MRI improving its accuracy and reproducibility as well as novel approaches that aim to quantify everything at once like MR Fingerprinting. There are many efforts in standardization, repeatability and reproducibility, as well as validating quantitative biomarkers across sites, vendors, and field strengths. Together, these advances point toward MRI that is faster, more accessible, more robust, and more quantitative—ultimately enabling broader global access and more reliable diagnostic and prognostic information.
Ivan Arias Hernandez, atmospheric sciences research center, albany, ny
biography
Ivan Arias Hernandez received his B.S. degrees in mathematics and electronic engineering and his M.S. degree in electronic engineering from Universidad del Norte, Barranquilla, Colombia, in 2012 and 2014, respectively, and his Ph.D. degree in electrical engineering from Colorado State University, Fort Collins, CO, USA, in 2023. He was visiting the Earth Observing Laboratory at NSF-NCAR in 2021 as part of the Advanced Study Program. He was a postdoctoral fellow at Colorado State University in the Radar and Remote Sensing Lab in 2023 and 2024. He was also a postdoctoral intern at the Naval Postgraduate School in the Clouds and Remote Sensing Lab in 2024 and 2025. He is currently a research faculty in the Atmospheric Science Research Center at the University at Albany, SUNY.
In-phase and quadrature time sequence observations of dual polarimetric weather radar in mixed-phase environments
Dual-polarized weather radars have become essential tools for monitoring and studying the evolution of precipitation particles in clouds. These instruments transmit and receive signals on both horizontal and vertical polarization channels, enabling the discrimination of particle shapes. This capability allows for the inference of hydrometeor types present in the atmosphere. On the other hand, in-phase and quadrature time sequences collected by weather radars every ms enable expanding dual-polarimetric measurements into the Doppler spectral domain. When particles move at different velocities due to differences in mass and shape, Doppler spectra exhibit bimodality, enabling the study of polarimetric signatures of particles in mixed-phase scenarios, such as rain, hail, and snow. Several case studies of different precipitation types will be presented, along with the mathematical formulation used to derive the dual-polarimetric Doppler spectrum. The digital signal processing techniques used to reduce the noise and enhance the quality of the Doppler spectrum in turbulent environments will also be presented.
Jiaxi hu, Atmospheric Sciences Research Center, Albany, NY
biography
Dr. Jiaxi Hu was born in China, in June 1990. He received the B.S. degree in atmospheric science from the Florida State University, Tallahassee, FL, USA, in 2013, and Ph.D. degrees in atmospheric science from the Texas A&M University, College Station, TX, USA, in 2018. From 2018 to 2025, he was a Research Scientist with the NOAA National Severe Storms Laboratory (NSSL) and the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO). He is currently a Research Faculty (tenure-track) with the Atmospheric Sciences Research Center, Albany, NY, USA. He works on novel radar and satellite processing algorithms to improve the understanding of atmospheric processes, especially on extreme weather events. He also works on aerosol cloud interactions and their implication to climate change.
Synergetic Use of the WSR-88D Radars, GOES-R Satellites, and Lightning Networks to Study Microphysical Characteristics of Hurricanes
This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.
david schvartzman, university of oklahoma
biography
David Schvartzman (Senior Member, IEEE) was born in Piracicaba, SP, Brazil, on March 17, 1988. He received the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Oklahoma, Norman, OK, USA, in 2015 and 2020, respectively, with a focus on polarimetric phased array radar. Dr. Schvartzman has held research positions supporting the NOAA National Severe Storms Laboratory (NSSL) and the Advanced Radar Research Center (ARRC) at the University of Oklahoma. At NSSL, he gained key insights into observational needs for improving weather warnings and forecasts and developed signal processing algorithms to enhance meteorological products for the operational US Weather Surveillance Radar (WSR-88D). He is currently an Assistant Professor with joint appointments in the School of Meteorology and the School of Electrical and Computer Engineering at the University of Oklahoma, affiliated with the ARRC. His work spans signal and array processing, radar calibration, and the development of advanced radar techniques for weather observation and severe weather detection.
Dr. Schvartzman is the recipient of several awards, including the 2023 IEEE R5 Outstanding Young Professional Award, the 2024 Research Excellence Award from the College of Atmospheric and Geographic Sciences at the University of Oklahoma, and the 2019 American Meteorological Society’s Spiros G. Geotis Prize. He is a Senior Member of the IEEE and a member of the American Meteorological Society (AMS) and its Scientific and Technological Activities Commission (STAC) on Radar Meteorology.
Radar Imaging with Digital Phased Arrays: From Beam Synthesis to Experimental Validation
Digital phased array radars enable a broader set of imaging and weather-surveillance modes than conventional mechanically scanned systems by providing fine control over the transmitted illumination pattern together with fully digital receive beamforming. This presentation will provide an overview of radar imaging modes that can be realized with digital phased arrays, with emphasis on experimental demonstrations using the fully digital Horus radar. Particular attention will be given to the use of one-dimensional and two-dimensional spoiled transmit beams, combined with digital beamforming on receive, to illuminate extended angular sectors while preserving the ability to form multiple narrow receive beams and recover spatial structure within the scene. The talk will discuss the underlying beamforming and beam-synthesis principles, practical design tradeoffs in shaping broadened transmit patterns, and validation through beam-pattern measurements collected in a near-field chamber. Experimental results will then be presented to illustrate how these modes can be implemented in practice and how they can support meteorological applications, including enhanced weather surveillance, improved spatial sampling of distributed targets, and new approaches for observing storm structure and precipitation. Overall, the presentation will highlight Horus as a flexible platform for exploring advanced radar imaging concepts that bridge computational imaging and next-generation meteorological radar observations.
Mark spencer, university of arizona
BIOGRaphy
Mark F. Spencer is a Professor of Optical Sciences and the inaugural holder of the Robert M. Edmund Endowed Chair in Optical Sciences within the James C. Wyant College of Optical Sciences at the University of Arizona. At large, he is a scientist/engineer who has spent his career working in various technical and administrative capacities. Mark began his career at the Air Force Research Laboratory, Directed Energy Directorate (2014-2021) after receiving his PhD from the Air Force Institute of Technology as a SMART Scholar. Before taking his current role in academia, he served as a Directed Energy Staff Specialist at Headquarters US Indo-Pacific Command (2021-2023), as well as Director of the Joint Directed Energy Transition Office and Principal Director (Senior Official) for Directed Energy within the Office of the Under Secretary of Defense for Research and Engineering at the Pentagon (2023-2025). Mark is an internationally recognized expert in directed energy (specifically, beam control and propagation for laser systems) and currently conducts research in unconventional imaging, sensing, and adaptive optics for defense and commercial applications. He is a Senior Member of Optica and a Fellow of SPIE.
Deep-Turbulence Limitations for Imaging, Sensing, and Adaptive Optics
Decades of outstanding research has led to an increased understanding of the underlying physics associated with deep turbulence. As a result, this talk provides a “cheat sheet” that parameterizes the limitations of deep turbulence in terms of imaging, sensing, and adaptive optics. Such a cheat sheet, for example, helps in comprehending recently published results that speak to the state of the art in directed energy and other active electro-optical applications. This talk will present these results in terms of the isoplanatic angle, Fried coherence diameter, Rytov number, and Greenwood frequency. Such parameters manifest from integrals involving the path-dependent refractive index structure parameter, C_n^2(z). In units of m^(-2/3), researchers often use C_n^2(z) alone to gauge the strength of turbulence. However, the distributed-volume nature of deep turbulence requires that researchers also solve these so-called path integrals to fully appreciate the underlying physics. This talk will conclude with insights into future research directions on this topic with an emphasis on computational imaging.
Chenghao Wang, university of oklahoma
biography
Dr. Chenghao Wang is an assistant professor in the School of Meteorology and Department of Geography and Environmental Sustainability at the University of Oklahoma. He obtained his Ph.D. degree in Civil, Environmental and Sustainable Engineering from Arizona State University. He was a postdoctoral research fellow at Stanford University and an inaugural New Map of Life fellow at Stanford Center on Longevity. Dr. Wang and his Sustainable URban Futures (SURF) Lab investigate the mechanisms of urban environments, their interactions with regional and global climate systems, and their interconnected impacts on energy use, emissions, and planetary health with both advanced physics-based numerical models and data-driven analytical approaches. He is a recipient of the NASA Early Career Investigator Award, NSF EPSCoR Research Fellowship, and American Meteorological Society STAC Outstanding Early Career Award. He currently chairs the IAUC Bibliography Committee and the AMS Committee on Meteorological Aspects of Air Pollution. He also served as a contributing author of the U.S. Sixth National Climate Assessment.
Remote sensing in urban climate research: From global patterns to process-based understanding
Remote sensing has fundamentally transformed urban climate research in recent decades. It enables consistent observations of land surface temperature, vegetation, and other surface properties across cities worldwide, from regional to global scales. These advances have supported a rapid expansion of empirical studies, revealing large-scale patterns in urban heat, vegetation cooling effects, and their dependence on climate conditions. This talk synthesizes recent work to highlight how remote sensing has reshaped our understanding of urban climate processes, particularly the role of urban vegetation in mitigating heat stress. However, the increasing reliance on satellite-based approaches also exposes important limitations, leaving critical questions regarding mechanisms, variability, and transferability unresolved. This talk discusses how process-based urban canopy models can be used alongside remote sensing to provide a mechanistic framework for interpreting these observed patterns and extending insights to processes and conditions that satellites cannot directly resolve. Recent developments in modeling urban tree–climate interactions will be presented to demonstrate how combining observationally informed inputs with process-based modeling helps reveal context-dependent cooling effects and improves predictive capability. Finally, emerging opportunities for advancing urban climate research are outlined, including next-generation remote sensing, improved characterization of urban structure, and closer integration between observations and models through hybrid data–model approaches.
Ji Yi, Johns Hopkins University
biography
Ji completed his undergraduate and PhD degrees in biomedical engineering at Tsinghua University in 2005 and Northwestern University in 2012. He started his independent faculty career at Boston University Medical Center, and is currently an Associate Professor of Biomedical Engineering and Ophthalmology, the Boone Pickens Rising Professor of Ophthalmology in Johns Hopkins University. Ji specializes in optical imaging, in particular developing multimodal volumetric optical imaging methods across large length scales of biological systems. His PhD research is focused on early gastroenterology (GI) cancer detection using light scattering, and started ophthalmic imaging in his postdoc training. He has made several impactful technical innovations, including inverse spectroscopic optical coherence tomography, visible light OCT, and oblique scanning laser ophthalmoscopy etc. More recently, his lab incorporated computational approach to address some of the limitations in multi-dimensional imaging data. Ji has contributed over 70 peer-reviewed journal articles, and is the co-inventor in 6 US and international patents. He has received numerous awards, including Baxter young investigator award (2013), JDRF postdoctoral fellowship (2014), Bright Focus foundation awards (glaucoma national award in 2017, macular degeneration award in 2018), Boston University KL2 career development award (2017) and Boston University Evans junior faculty merit award (2018). He is leading and participating several NIH-funded studies in both translational and basic science research. His leadership include director of P30 microscopy core in Wilmer Eye Institute, program director of BME 3+1, and Wilmer AI center founding committee member.
Computational synthetic aperture imaging in the living retina
The eye offers a unique non-invasive window for accessing single-cell level structures and functions of the central nervous system (CNS) throughout the retina. However, strong and space-varying ocular aberrations, along with limited volume rates, challenge large-scale cellular imaging in living eyes and stymie the full potential of possible biological and pathological studies in retina. In this talk, a computational synthetic aperture imaging methods will be introduced termed plenoptic illumination scanning laser ophthalmoscopy (PI-SLO). It allows 3D fluorescent retinal imaging that features high-speed, widefield, volumetric single-cell imaging with low phototoxicity. The synthetic aperture method not only reconstructs 3D retinal volume, but also enables digital aberration correction across a >20º FOV with >23 Hz volume rate. We leverage this structural and functional imaging modality to investigate three key aspects of CNS physiology through the living mouse retina, including: microglial process dynamics, vascular perfusion, and light evoked calcium fluxes in inner retinal neurons. The computational retinal imaging method presented here is a versatile non-invasive platform for in vivo investigation of retinal and CNS physiology at the cellular level.