Ram Kumar Selvaraju is a Researcher at Uppsala University , affiliated with the Department of Medicinal Chemistry and the Preclinical PET-MRI Platform . With a background in biotechnology and nuclear medicine, his work focuses on molecular imaging techniques for diabetes and cancer research. Education : PhD in Pharmacy (Uppsala University, 2015), MSc in Medical Nuclide Techniques (Uppsala University, 2011), BTech in Biotechnology (VIT University, 2009) Research Areas : Development of PET/SPECT/CT tracers for beta cell imaging , diabetes , and neuroendocrine cancer . Techniques include autoradiography, small animal modeling, and image analysis with PMOD/AMIRA. Publication Trends : Recent work emphasizes theranostic nanoprobes , dual-nuclide radiopharmaceuticals , and beta cell mass quantification using [11C]5-HTP and [68Ga]Exendin-4. Collaborations span oncology, diabetes, and imaging technology development.
Tristan van Leeuwen is a Professor of Computational Inverse Problems at Utrecht University's Faculty of Science, within the Mathematical Institute's Department of Mathematical Modeling. He holds a MSc in Computational Science (2006) and a PhD in Geophysics (2010). His career includes postdoctoral roles at the University of British Columbia and Centrum Wiskunde & Informatica (CWI), followed by faculty positions at Utrecht University and group leadership at CWI. His research focuses on inverse problems, scientific computing, imaging reconstruction, and computational methods for geophysics and medical imaging. Key areas include wave-equation inversion, tomographic reconstruction, and uncertainty quantification. Notable contributions span seismic inversion techniques, convex optimization frameworks for tomography, and deep learning applications in imaging. His publications emphasize methodologies like wavefield reconstruction inversion, convex programming for shape sensing, and Bayesian approaches for uncertainty analysis. He collaborates with institutions like CWI and the University of British Columbia, contributing to open-source tools like Tomosipo for tomography. His work bridges theoretical mathematics with practical applications in geophysics, medical diagnostics, and industrial inspection.
Dr. Amin Zehtabian is a Researcher in the Ewers Group - Membrane Biochemistry at the Department of Biochemistry, Freie Universität Berlin. He holds a PhD in Electrical Engineering (2016) with a focus on Pattern Recognition, Computer Vision, and Multispectral Signal Analysis. His work integrates computational methods with biological research, including quantitative microscopy data analysis, image segmentation, and deep learning applications in neuroscience and cell biology. Education: PhD in Electrical Engineering (minor in Communication Systems), 2016. Research Interests: Dr. Zehtabian specializes in computational biology and bioimaging, with expertise in 2D/3D image segmentation, neural networks, noise reduction, and genetic algorithms. His current projects explore cargo trafficking in recycling endosomes, circadian rhythm dynamics, and neuronal morphology analysis using unsupervised learning techniques. Publications: His recent work spans advancements in membrane biochemistry (e.g., ARF1 compartment dynamics), circadian protein turnover, and deep learning applications in superresolution microscopy. He also contributes to hyperspectral imaging innovations for environmental and medical diagnostics. Advising/Grants: No explicit grants or advisees listed, but coordinates an Open Position announcement for a Scientist/Project Coordinator at FU Berlin (2025). Labs/Teams: Core member of the Ewers Group, focusing on membrane biochemistry and quantitative imaging.
Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.
J. Webster Stayman is an Associate Professor in both the Department of Biomedical Engineering and the Department of Electrical & Computer Engineering at Johns Hopkins University. He also serves as Co-Director of the Biomedical Engineering Master's Program and leads the Advanced Imaging Algorithms and Instrumentation Lab. His research bridges engineering principles with medical imaging applications, focusing on developing advanced imaging systems and algorithms that optimize diagnostic outcomes while minimizing radiation exposure. Dr. Stayman received his PhD in Electrical Engineering from the University of Michigan in 2003, following an MS in Electrical Engineering from the same institution in 1998. He completed his undergraduate education with a BS in Computer & Systems Engineering from Rensselaer Polytechnic Institute in 1995. His research focuses on medical imaging systems modeling, design, and optimization, particularly for x-ray CT, cone-beam CT, and phase-contrast CT. A key aspect of his work involves integrating patient-, task-, and device-specific information into imaging workflows to optimize image quality for specific clinical tasks. He applies signal processing, estimation theory, and optimization techniques to develop sophisticated image reconstruction algorithms that can produce high-quality images from low-fidelity or sparse data. Analysis of his recent publications (2023-2025) reveals a strong trend toward incorporating advanced machine learning techniques, particularly diffusion models, into medical imaging. His work spans material decomposition, artifact reduction, spectral CT, and optimization of imaging systems. Many papers focus on improving CT reconstruction through novel algorithms that leverage deep learning while maintaining physical accuracy of the imaging process. His research shows increasing emphasis on quantitative imaging, uncertainty quantification, and developing tools that bridge the gap between machine learning approaches and traditional model-based reconstruction. Dr. Stayman's research has been supported by significant NIH funding, including a $2.6 million grant in 2015 to develop improved CT imaging hardware and software for patient-specific, low-dose CT scans. During the pandemic, he adapted his "Build an Imager" course for virtual delivery, demonstrating innovation in both research and education. He has made notable contributions to 3D printing of patient-specific phantoms for CT validation studies, advancing the field's ability to assess image quality and algorithm performance.
Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu
Thomas Lindner is a Professor in the Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the University of Hamburg's Faculty of Medicine. His research focuses on advanced neuroimaging techniques, particularly arterial spin labeling (ASL) and MRI-based perfusion analysis, with applications in cerebrovascular diseases, stroke, and neuro-oncology. He leads efforts to standardize ASL protocols and improve diagnostic accuracy for conditions like intracranial aneurysms and gliomas. Lindner's work bridges clinical practice and cutting-edge imaging technology, emphasizing translational research and collaborative initiatives like the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). His studies often involve population-based analyses (e.g., Hamburg City Health Study) and explore socioeconomic factors influencing disease incidence. Lindner also investigates intraoperative MRI methods for pediatric neurosurgery and develops non-contrast-enhanced imaging approaches to reduce patient exposure to contrast agents. His contributions span technical innovations in MRI hardware/software, clinical trial design, and global standards for neuroimaging reporting and data sharing.
Christoph Heinzl is Privatdozent at Technische Universität Wien, Faculty of Informatics, Institute of Visual Computing & Human-Centered Technology, and an associated researcher at University of Applied Sciences Upper Austria, Wels. His work bridges scientific visualization and industrial X-ray computed tomography, with applications in non-destructive testing and metrology. Education & qualifications: Heinzl holds a Dipl.-Ing.(FH) degree and a doctorate (Dr.) from TU Wien; in 2021 he completed his professorial dissertation (Habilitation) on visualization and analysis of XCT data. Research interests revolve around interactive visual analysis of high-dimensional and multimodal industrial data, uncertainty visualization, metrological evaluation of CT data, and development of immersive analytics workspaces for materials characterization. Across 60+ peer-reviewed publications since 2006 he has advanced techniques for porosity quantification, defect tracking, metal-artefact reduction, and comparative visualization of 3-D volumes, often in close cooperation with the Austrian COMET centre “EXCELLENCE IN COMET” and the K-project “ADVANCED METROLOGY”. Awards & recognition: While no major personal prizes are listed, Heinzl has served as paper co-chair and editorial guest editor for leading visualization venues, indicating peer recognition. Teaching & supervision: He regularly offers bachelor thesis topics such as “ImNDT: Immersive Workspace for Analysis of Multidimensional NDT Data” and teaches courses on visualization and computer graphics; specific PhD students are not named in the supplied sources. Funding & labs: Research is embedded in the university’s Visual Computing research area and supported by national competence centres for metrology, providing access to state-of-the-art XCT facilities and VR/AR laboratories.
Richard Heck is a Professor at the Ontario Agricultural College within the School of Environmental Sciences at the University of Guelph. He holds expertise in soil morphology quantification, leveraging advanced technologies like 3D soil modeling, high-resolution remote sensing, and X-ray computed tomography. His research spans scales from local catchments to sub-pedon levels, collaborating globally with researchers across multiple continents. Education: B.S.A., M.Sc., and Ph.D. from the University of Saskatchewan. Research Interests: Quantification of soil morphology using techniques such as thermal imaging, magnetic induction, and LiDAR. His work focuses on soil structure characterization, drainage conditions, and the application of imaging technologies like micro-CT. He emphasizes interdisciplinary approaches, integrating pedology, micromorphology, and computational methods. Publications: Recent work includes advancements in digital soil mapping methodologies, magnetic susceptibility applications, and soil porosity analysis in diverse environments. His articles highlight innovations in sample size optimization, drainage classification, and agricultural soil management. Awards: Honored as a Corresponding Member of the Pernambuco Academy of Agronomic Sciences and the Brazilian Academy of Agronomic Sciences, as well as receiving the Ontario Innovation Trust’s Certificate of Recognition for contributions to soil variability research. Funding & Grants: Active grants include NSERC DG (2019–2024), NSERC Engage (2019–2020), and OMAFRA support. His lab hosts the Canadian Soil Thin Section Collection and collaborates internationally on projects. Labs & Teams: His laboratory at Alexander Hall (Room 140) focuses on cutting-edge imaging and soil analysis, advancing techniques like µCT and spectral imaging to address global soil challenges.
Dennis R. Schaart is a Professor and head of the Medical Physics & Technology section at the Department of Radiation Science & Technology, Faculty of Applied Sciences, Delft University of Technology (TU Delft). He is also a member of the R&D Program Board of the Holland Proton Therapy Centre (HollandPTC), highlighting his significant role in advancing clinical radiation technologies. His work bridges fundamental physics with medical applications, particularly in imaging and therapy. His primary research interests include Medical Physics, Radiation Oncology, Medical Imaging, Radiation Detection, Dosimetry, and Biomedical Engineering . He specializes in positron emission tomography (PET), time-of-flight methods, proton therapy, and scintillation detector development. His expertise in Monte Carlo simulation and experimental physics enables rigorous evaluation and innovation in detector systems and imaging protocols. The recent publications (2025–2021) reveal a strong trend toward photon-counting X-ray and PET detectors , proton therapy optimization , and novel scintillator materials . There is a clear emphasis on improving spatial, temporal, and energy resolution in imaging, with applications in both diagnostics and treatment planning. The integration of machine learning and Monte Carlo simulations further enhances the predictive and analytical power of his research. Scientific Awards: SNMMI 2015 International Best Abstracts Award Awarded for the highest number of citations for an article published over 2004–2008 Most cited paper in preceding five years (GATE V6 paper) Recognition at Trace 'n Treat conference for radionuclide state determination Dennis Schaart has (co-)authored over 100 journal papers and is a frequently invited speaker, indicating strong leadership and influence in the medical physics community. While no direct mention of students is found, his leadership role and extensive publication record suggest active supervision and mentorship. He is involved in national advisory roles, including serving on committees for the Ministry of Economic Affairs, reflecting broader impact beyond academia. His work is supported by collaborations with institutions like Philips, CERN, and various medical centers. Laboratories and Research Groups: He leads the Medical Physics & Technology research group within the Radiation Science & Technology department at TU Delft. The group focuses on developing and evaluating novel detector systems for medical imaging and therapy, using both experimental and computational approaches. The lab is equipped for scintillator characterization, detector prototyping, and advanced simulations, particularly using the GATE platform.
Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.
Kaitlin Keegan is an Assistant Professor at the University of Nevada, Reno within the Graduate Program of Hydrologic Sciences. Her research focuses on the material properties of ice and their role in interpreting paleoclimate records from ice cores. Ph.D. in Materials Science and Engineering from Dartmouth College (2014) B.S. in Materials Science and Engineering from the University of Cincinnati (2009) Her work investigates snow metamorphism, firn compaction, and meltwater interactions with polar ice sheets using fieldwork, microstructure analysis (e.g., micro-CT), and computational modeling. She specializes in understanding how ice microstructure preserves climate history and impacts meltwater retention. Recent publications highlight her interdisciplinary approach combining glaciology, materials science, and topological data analysis. Key projects include firn microstructure evolution at Taylor Dome, meltwater infiltration feedbacks, and physics-based firn compaction models. Laboratory members include graduate students Drake McCrimmon (PhD), Dylen Swan (MS), and undergraduate researcher Samantha Regalado, alongside alumni Ian McDowell (PhD 2024) and Justin Toller (MS 2023).
Jean-François Giovannelli is a Professor at IMS Bordeaux , affiliated with the Université de Bordeaux . His work focuses on Signal and Image Processing within the SPECTRAL team. Collaborations span institutions like CEA , CNRS , and industry partners ( STMicroelectronics , Stellantis ), emphasizing Bayesian methods and MCMC algorithms for diverse applications. Current Affiliation : Professor, IMS Bordeaux, Université de Bordeaux Research Group : Signal and Image Processing Team : SPECTRAL Collaborations : CEA, CNRS, CESTA, Thales, NXP Research Interests include: Bayesian inference for inverse problems MCMC sampling techniques Signal/image reconstruction Mass spectrometry data analysis Adaptive optics in astronomy Medical imaging algorithms Article Trends show a focus on Bayesian modeling across disciplines: biomedical data, astronomical imaging, and microwave mapping. Key methods include MCMC samplers , regularized inversion , and statistical validation . Professional Contributions involve developing tools like NiftyRec for tomography and advancing Adaptive Optics restoration algorithms. His work bridges theoretical statistics and practical applications in healthcare, space, and defense.
Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.
Dr. James Atlas is a Senior Lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury's Faculty of Engineering, where he has been employed since July 2018. His primary affiliations include full-time academic responsibilities at this institution. His research spans: Core CS : Artificial intelligence, machine learning, constraint optimization Systems : Distributed and high-performance computing Applied domains : Medical data analysis (CT reconstruction, health prediction), earth/space exploration (flood mapping, environmental monitoring), and multi-agent systems Education : Curriculum development, threshold concept gamification Recent publication analysis (2019-2025) reveals dominant themes in deep learning applications for medical imaging (spectral CT reconstruction, bioacoustic analysis) and environmental sensing (3D tree modeling, vineyard monitoring, flood prediction), with consistent interdisciplinary collaboration. He actively supervises 10 graduate students researching: Machine learning for global flood exposure mapping CT reconstruction algorithms and artifact correction EMG-EEG hybrid prosthetics Hierarchical reinforcement learning Automated 3D digital tree modeling Bioacoustic monitoring systems
