Elisabeth A. Wilde is an Associate Professor in the Department of Neurology at the University of Utah , with adjunct appointments in Physical Medicine & Rehabilitation and Radiology & Imaging Sciences. She serves as a Health Research Scientist at the VA Salt Lake City Healthcare System and directs the Neuroimaging Core for the DoD/VA LIMBIC-CENC consortium. Dr. Wilde co-leads the ENIGMA Working Group for TBI and contributes to the International Common Data Elements (CDE) initiative . PhD in Brigham Young University Postdoctoral Fellowship at University of Michigan Medical Center Research interests focus on advanced neuroimaging for TBI diagnosis, prognosis, and therapeutic monitoring. Key areas include: White Matter Integrity in Military TBI Neuroplasticity in Pediatric Brain Injury Neuroimaging Biomarkers for Concussion Neurodegenerative Patterns in Veterans Neuroimaging Harmonization Across Cohorts Sensorimotor Integration in Athletes Scientific contributions span over 140 peer-reviewed publications with a focus on ENIGMA consortium meta-analyses, LIMBIC-CENC studies, and military TBI outcomes. Her work integrates: Multimodal MRI/DTI Analysis Machine Learning for Aging Biomarkers Cognitive-Imaging Correlations Longitudinal TBI Trajectory Studies Neuroimaging in Sports Concussion PTSD-TBI Comorbidity Research
Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.
Donald R Cantrell, MD, PhD is an Assistant Professor in Radiology (Interventional Neuroradiology) and Neurology (Stroke and Vascular Neurology) at Northwestern University's Feinberg School of Medicine. He is affiliated with the Institute for Artificial Intelligence in Medicine's Center for Computational Imaging and Signal Analytics, Northwestern University Clinical and Translational Sciences Institute (NUCATS), and provides clinical services at Northwestern Memorial Hospital and Shirley Ryan AbilityLab. His academic training includes: PhD from Northwestern University (2009) MD from Northwestern University (2011) Transitional Internship at Saint Francis Hospital, Evanston (2012) Radiology Residency at Northwestern University McGaw Medical Center (2016) Diagnostic Neuroradiology Fellowship at Northwestern University McGaw Medical Center (2017) Dr. Cantrell's research integrates interventional neuroradiology with advanced computational techniques , focusing on stroke thrombectomy optimization, health disparities in neurovascular care, and novel imaging methodologies. His work bridges clinical neurology , medical AI , and signal analytics to improve outcomes in vascular neurology through computational modeling and registry-based analytics. Recent publications reveal a concentrated effort in applying machine learning to neurovascular interventions, with emphasis on predictive modeling for thrombectomy outcomes, racial disparity analysis in stroke treatment, 4D flow MRI hemodynamic assessment, and fluoroscopy-based procedural guidance systems. This research leverages large-scale clinical registries and advanced imaging modalities to address critical gaps in stroke care delivery and technical precision. He actively contributes to the Center for Computational Imaging and Signal Analytics in Medicine, which develops AI-driven solutions for medical imaging interpretation and signal processing. The center's work focuses on translating computational innovations into clinical practice for enhanced diagnostic accuracy and procedural efficacy in neurointerventional radiology.
Georges El Fakhri is a Professor of Radiology & Biomedical Imaging , Therapeutic Radiology , and Biomedical Informatics & Data Science at Yale University. He serves as Director of the PET Center and Vice Chair for Scientific Research in the Department of Radiology & Biomedical Imaging. Education: PhD in Medical Physics, University of Paris XI MMSc in Physiology & Neuroscience, University of Paris XI MSc in Biomedical Engineering, University of Paris XI MSc in Electrical & Computer Engineering, University of Texas ME in Electronics & Mechanics, Ecole Centrale His research focuses on advancing PET/MRI hybrid imaging , machine learning for medical imaging , and quantitative image analysis in neurology, oncology, and psychiatry. Recent work explores AI-driven tumor segmentation , low-dose PET denoising , and biomarker development for Alzheimer's disease. Key collaborations include frequent co-authors Marc Normandin , Jinsong Ouyang , and Xiaofeng Liu . He contributes to Yale Biomedical Imaging Institute and leads initiatives in theranostic digital twins and precision radiopharmaceutical therapies .
Nicha Dvornek is an Assistant Professor at Yale School of Medicine in the Department of Radiology & Biomedical Imaging and affiliated with the Image Processing & Analysis Group and Yale Biomedical Imaging Institute. She holds a PhD, MPhil, and MS from Yale University, and a BS from Johns Hopkins University. Her research focuses on autism spectrum disorder, biomedical engineering, and neuroimaging, particularly using machine learning for analyzing fMRI and PET data. Education: • PhD, Yale University (2012) • MPhil, Yale University (2009) • MS, Yale University (2007) • BS, Johns Hopkins University (2006) Her work applies advanced machine learning techniques to medical imaging, including rotation-equivariant networks, GANs for motion correction, and transformer-based models for fMRI analysis. She has received recognition through awards like the James Hudson Brown Fellowship and Best Paper Award at MLMI 2019. Scientific Awards: Best Paper Award, International Workshop on Machine Learning in Medical Imaging (2019) James Hudson Brown – Alexander Brown Coxe Postdoctoral Fellowship (2014) Nicha actively contributes to clinical research through trials related to autism and medical imaging. She is part of Yale’s Bioimaging Sciences division, advancing tools for image processing and analysis.
Nicolas Guehl is an Assistant Professor of Radiology and Biomedical Imaging at Yale School of Medicine , specializing in advanced PET imaging techniques for neurodegenerative disease research. His work focuses on tau protein quantification , receptor occupancy mapping , and radiotracer development for conditions like Alzheimer's disease and multiple sclerosis. Key Research Areas: Dynamic PET imaging protocols Kinetic modeling for brain disorders Neurodegenerative biomarker discovery Radiosynthesis automation Recent Article Trends: Improved tau burden measurement Receptor occupancy analysis for drug development Comparative studies with [15O]water Stress-induced striatal connectivity mapping Contact: nicolas.guehl@yale.edu
Antonio Esposito serves as Full Professor of Radiology at Vita-Salute San Raffaele University in Milan, Italy, holding multiple leadership positions at IRCCS Ospedale San Raffaele including Scientific Director of Clinical Trial Center and Deputy Scientific Director IRCCS. With over 250 peer-reviewed publications, his academic career spans from Researcher positions to his current professorship, demonstrating continuous advancement in the field of medical imaging. MD degree (summa cum Laude), 1997-2003, Vita-Salute San Raffaele University Specialization Degree in Radiology (summa cum Laude), 2003-2007, Vita-Salute San Raffaele University Professor Esposito maintains extensive expertise in cardiovascular and oncological applications of advanced imaging technologies, particularly MRI and CT. His research focuses on clinical and preclinical imaging, with specialization in cardiovascular MRI studies and CT examinations. He leads the Preclinical Imaging Facility equipped with high-resolution CT, 7T MRI, Optical, US, and Photoacoustic imaging technologies for experimental disease modeling. His work bridges clinical practice with cutting-edge research methodologies, emphasizing translational applications of imaging technologies in cardiovascular medicine. His recent publications reveal a strong emphasis on cardiac imaging applications, particularly in valvular heart disease, cardiomyopathies, and the integration of machine learning with radiomics. The research demonstrates sophisticated analysis of left ventricular outflow tract dynamics, novel diagnostic approaches for cardiac sarcoidosis, and genetic markers associated with cardiomyopathy. His work consistently combines clinical insights with advanced imaging techniques to address complex cardiovascular conditions. President of the Italian College of Cardiac Radiology by SIRM (2019, 2021) Board member of European Society of Cardiac Radiology Scientific Committee (2017-2020) National Scientific Qualification for Full Professor of Radiology (2017) National Scientific Qualification for Associate Professor of Radiology (2014) Best scientific presentation at National Meeting of Thoracic and Cardiac Imaging (2005) Professor Esposito actively contributes to doctoral education across multiple programs including Molecular Medicine and Cognitive Neuroscience at UNISR. His research leadership extends to directing the Strategic Programme Cardiovascular Radiology and Preventive Imaging, overseeing more than 1,000 cardiovascular MRI studies and 4,000 CT examinations annually. He serves as reviewer for prestigious journals including JACC: Cardiovascular Imaging, European Radiology, and European Heart Journal, demonstrating his standing in the international imaging community. He leads the Preclinical Imaging Facility at IRCCS San Raffaele Hospital, a comprehensive research center equipped with high-resolution CT, 7T MRI, Optical, US, and Photoacoustic imaging technologies dedicated to experimental disease modeling in small animals. Additionally, he directs the Cardiovascular Imaging Functional Unit, providing extensive clinical imaging services while advancing research in cardiovascular applications of medical imaging.
Dr. Alexandru V. Korotcov is an Assistant Professor in the Department of Radiology & Radiological Sciences at the Uniformed Services University of the Health Sciences (USUHS) School of Medicine since 2021, concurrently serving as Senior MRI Scientist and Advisor at the Biomedical Research Imaging Core since 2012. His expertise bridges experimental physics and computational medicine with military healthcare applications. His academic foundation includes: DSc in Physics and Mathematics, State University of Moldova (2003) MSc in Physics, State University of Moldova (1996) Postdoctoral Fellowship in Molecular Imaging, Howard University (2006-2008) Postdoctoral Fellowship in Nanotechnology, National Taiwan University of Science and Technology (2004-2006) Dr. Korotcov's research integrates medical imaging, AI-driven analysis, and nanotechnology to address traumatic brain injury, cancer diagnostics, and drug development. With over 20 years of experience, he pioneers pre-clinical MRI techniques and machine learning pipelines for military-relevant conditions, emphasizing translational impact from animal models to clinical practice. His work particularly focuses on developing quantitative imaging biomarkers for blast injuries and neurological disorders. His publication record demonstrates consistent innovation in applying advanced MRI and deep learning to military medicine challenges, with recent work emphasizing traumatic brain injury biomarkers, neuropathology modeling, and AI-optimized image analysis. The research trajectory shows increasing integration of multimodal imaging with computational methods to solve complex diagnostic problems in resource-constrained environments. His recognition includes: Certificate of Merit, Radiological Society of North America (2009) 3rd Place, Howard University Research Day Abstract Competition (2010) World Molecular Imaging Congress Poster Highlight (2011) Certificate of Appreciation, TriService Nursing Research Program (2017) Through the Biomedical Research Imaging Core, Dr. Korotcov mentors researchers and collaborates with NIH, Johns Hopkins, and Georgetown University on grant-funded projects. His advisory role involves experimental design consultation and advanced data analysis for military medical studies, particularly those involving neurotrauma and cancer imaging. He leads the Biomedical Research Imaging Core's development of specialized MRI protocols and AI-powered analysis tools for military applications, including blast injury assessment and cancer diagnostics in deployed settings. The core serves as a critical resource for USUHS researchers conducting translational studies relevant to combat casualty care.
Seth A. Schobel, PhD, serves as Associate Professor and Scientific Director of the SC2i (Surgical Critical Care and Injury Innovation) within the Department of Surgery at Uniformed Services University of the Health Sciences (USUHS) School of Medicine. His academic appointment reflects his dual expertise in computational biology and clinical trauma research. Dr. Schobel's educational background includes a PhD in Bioinformatics, Computational Biology and Genomics from the University of Maryland (2015), an MS in Bioinformatics from Johns Hopkins University (2009), and dual BS/BA degrees from the University of Maryland (2001). His research program bridges computational science with clinical military medicine, focusing on host-pathogen interactions, wound healing mechanisms, and inflammatory responses in combat casualties. His laboratory develops cloud-based computing environments for big data analysis of wound healing trajectories, with particular emphasis on machine learning applications for clinical decision support. Current projects analyze microbial bioburden in battlefield injuries, cytokine networks in trauma patients, and predictive modeling for complications like wound infections and acute kidney injury. The SC2i team under his direction integrates multi-omics data with clinical parameters to create operational tools for battlefield medicine. Dr. Schobel's publication record demonstrates consistent translational research output, with recent work focusing on microbial colonization effects on wound healing, machine learning models for infection prediction, and neuroinflammatory consequences of systemic trauma. His collaborative approach involves partnerships across military medical centers and academic institutions. As Scientific Director of SC2i, he leads a multidisciplinary team developing clinical decision support tools that translate complex biomarker data into actionable clinical insights for military surgeons. His work exemplifies precision medicine applications in austere combat environments, with emphasis on real-time data integration for improved casualty outcomes.
Jiasen Ma, Ph.D., is a Radiation Oncology Medical Physicist at Mayo Clinic in Rochester, Minnesota. He holds certifications from the American Board of Radiology and leads initiatives in computational radiobiology. His research optimizes proton therapy and radiation dosimetry for cancers using Monte Carlo methods. Education: Residency, Radiation Oncology Medical Physics, Mayo Clinic College of Medicine (2018) Ph.D., University of Chicago (2009) B.S., University of Science and Technology of China (2003) Research Interests: Dr. Ma focuses on advancing radiation therapy through proton beam optimization, radiobiological modeling, and clinical applications for oncology. Key areas include linear energy transfer (LET) optimization, ultra-hypofractionated treatments, and Monte Carlo dose calculation to improve cancer outcomes and minimize toxicity. Awards & Honors: Robert G. Sachs Fellowship (2003) Professional Affiliations: Chair, Computational Radiobiology of Radiation Oncology (2019–present) Vice-Chair, Genitourinary Disease Site Group (2019–present) Member, American Society for Radiation Oncology (2019–present) Member, American Association of Physicists in Medicine (2013–present) Clinical Expertise: Specializes in proton therapy, brachytherapy, and image-guided radiotherapy for prostate, rectal, and lung cancers.
Oleg Stanislavovich P'yanykh is a Professor in the Department of Data Analysis and Artificial Intelligence at the Faculty of Computer Science, National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2012 with 13 years of scientific and teaching experience. P'yanykh maintains active roles as a Guest Editor for both the American Journal of Roentgenology (since 2011) and Pattern Recognition (since 2010), and serves as a permanent member of the International DICOM Committee working group. His educational background includes a PhD in Computer Science from Louisiana State University (1998), an MS with honors in Applied Mathematics and Physics from Moscow State University (1994), and a Diplôme d'Etudes et de Recherches in Philosophy from French University College, Moscow State University and Sorbonne University (1994). P'yanykh's research spans medical informatics, image analysis and processing, medical information systems and standards (particularly DICOM and PACS), computer-aided diagnostics, teleradiology, machine learning, and control theory. His work bridges computer science with practical medical applications, focusing on improving medical imaging systems and data analysis. His publication record shows consistent contributions from 1997 through 2024, with recent work emphasizing scheduling algorithms, machine learning interpretability, human knowledge modeling, and medical image quality assessment. His research demonstrates a clear trajectory from foundational signal processing work to increasingly applied medical informatics solutions. Bonus for publication in an international peer-reviewed scientific journal (2019-2021) Bonus for publication in an international peer-reviewed scientific journal (2017-2018) Bonus for an article in a foreign peer-reviewed journal (2013-2015) P'yanykh has supervised student research including Viktor Sergeevich Lopatin's bachelor's thesis on 3D medical image processing and Ksenia Dmitrievna Loginova's master's thesis on medical image quality assessment. He has taught courses including Big Data and Machine Learning in Healthcare across multiple academic years (2020-2026) and Medical Informatics (2020-2022), reflecting his focus on applying computational methods to healthcare challenges.
Peter Riis Hansen is a Clinical Professor in the Department of Clinical Medicine at the University of Copenhagen, specializing in Internal Medicine with a focus on Cardiology. His work is conducted in affiliation with the Capital Region of Denmark (Region H), as indicated by his institutional email address. Dr. Hansen's research program spans multiple clinical domains with particular emphasis on cardiology, periodontal disease, vaccine safety, and genetic factors in disease susceptibility. His work demonstrates an interdisciplinary approach that bridges clinical medicine with epidemiological methods and data science applications. Recent publications reveal a strong focus on translational research that addresses both fundamental disease mechanisms and practical clinical applications. Analysis of his publication trends shows significant contributions to understanding vaccine safety profiles (particularly regarding mRNA COVID-19 vaccines), immune responses in periodontal disease, and the relationship between genetic factors and cardiometabolic conditions. His research often involves large-scale population studies in Danish cohorts with international collaborative elements, including comparative analyses with other Nordic countries and the Czech Republic. With 285 total research outputs comprising 249 journal articles, 12 commentaries/debates, 10 reviews, and various other scholarly contributions, Dr. Hansen maintains an active research profile. His work has generated considerable scientific discussion, with multiple publications receiving attention from numerous news outlets and substantial engagement across social media platforms, indicating relevance to both academic and public health discourse.
Christina Rostrup Kruuse serves as a Clinical Professor in the Department of Clinical Medicine at the University of Copenhagen, with clinical operations based at Rigshospitalet (Copenhagen University Hospital) across Glostrup and Copenhagen N. campuses. Her dual appointment bridges academic neurology and hospital-based clinical practice, maintaining active research and patient care roles as evidenced by 160 research outputs including 8 new 2025 publications. Her research program centers on stroke pathophysiology and cerebrovascular disorders, with significant contributions to biomarker validation (C-reactive protein, cystatin C), geographical disparities in acute stroke care, and hematologic risk factors like iron deficiency. She integrates emergency medicine perspectives through CPR training studies while advancing neuroimaging via AI applications in MRI analysis. Her work consistently addresses translational challenges from basic mechanisms (ischemic preconditioning effects on clotting) to population-level healthcare delivery (thrombolysis access mapping). Recent 2025 publications reveal intensified focus on interdisciplinary stroke research, combining neurology with hematology, emergency medicine, and data science. Key trends include systematic analysis of rare stroke associations (Terson syndrome, cerebral venous thrombosis), validation of diagnostic alternatives (GFR measurement techniques), and critical evaluation of AI methodology (spectrum bias in deep learning). This portfolio demonstrates exceptional breadth across stroke subtypes, risk factors, and care continuum stages. No scientific awards were documented in the source materials. While her publication record indicates substantial collaborative research, specific details regarding student supervision, grant funding, or laboratory leadership were not provided in the available information.
Andreas Grontoudis serves as Assistant Professor in the Department of Computer Science and Engineering at the School of Sciences, European University of Cyprus since October 2007, following prior appointments as Assistant Professor at Cyprus College (2003-2007) and senior software development manager at Quad Computer Services (2001-2003). His academic credentials include: PhD in Computer Science, University of Sheffield (2000) - Thesis: X-machine based specification and design for testing of the CATV protocol MSc (Eng) in Computer Science, University of Sheffield (1994) - Thesis: An initial approach to X-machine specification of Distributed systems BSc in Computer Science, University of East Anglia (1993) - Thesis: A computer automated measurement system Dr. Grontoudis specializes in bridging theoretical computer science with practical applications, particularly in protocol specification using X-machines, quality assurance systems for medical imaging, and disaster monitoring technologies. His research evolved from foundational work in formal methods for protocol testing to applied solutions in diagnostic imaging toolboxes and integrated surveillance systems for forest fires/disasters using aerial and space-based platforms. Recent work focuses on educational software for IT vocational training. His publication history demonstrates consistent interdisciplinary contributions spanning biomedical engineering, environmental monitoring, and software engineering, with emphasis on translating theoretical models into real-world safety-critical applications. Professional recognition includes: Third Party Developer Pegasus Opera II award (Pegasus Software, 2001) First Certificate in English (University of Cambridge, 1985) Research leadership spans multiple EU-funded initiatives: Interfaces project (2016-2020): Website and learning platform management for EU interdisciplinary music program JOBIT project (2015-2017): Researcher for Erasmus+ vocational training software development YPE NEPRO0204_02 (2004-2006): Research assistant and software programmer for Cyprus Research Foundation He maintains active scholarly engagement through his Google Scholar profile and continues developing applied solutions at the intersection of computer science and critical domain applications.
Claire Grover is a Senior Research Fellow at the Institute of Language, Cognition and Computation within the School of Informatics at the University of Edinburgh. Her research bridges computational linguistics with real-world applications in public health, urban studies, and digital humanities through advanced Natural Language Processing techniques. Her primary research interests include Natural Language Processing, Text Mining, Information Extraction, Named Entity Recognition, and Corpus Linguistics. She specializes in developing robust NLP methodologies for domain-specific challenges, particularly in clinical text analysis, historical document processing, and geospatial text mining. Her work emphasizes practical implementation and evaluation of NLP systems for reliable real-world deployment. Recent publications (2021-2025) demonstrate a clear trend toward interdisciplinary NLP applications: using topic modeling of local news to study neighborhood health effects, evaluating clinical NLP tools for stroke phenotype extraction, and scaling historical text analysis frameworks. These works consistently address methodological reliability and cross-domain adaptability of text analytics systems. No scientific awards were mentioned in the available information. Dr. Grover has secured significant research funding as Principal Investigator for projects including ‘Leveraging routinely collected and linked research data to study mental disorders’ (MRC), ‘Empowering elderly people in healthcare conversations’ (EU), and ‘Words on the Street: Digital Literary Cityscape’ (AHRC). Her collaborative network spans computer science, healthcare, and social sciences, with evidence of student supervision through ‘Supervised Work (1)’. She actively contributes to the Language, Interaction, and Robotics group and Edinburgh Neuroscience initiative, participating in interdisciplinary teams that combine computational expertise with domain knowledge. Her media-covered Trading Consequences project exemplifies her approach to transforming historical trade analysis through text mining.
