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Conor JudgeView profile

Senior Lecturer

• Artificial Intelligence in Medicine
• Data Analytics in Healthcare
• Nephrology
• +3 more

Dr. Conor Judge is a Consultant Nephrologist at Galway University Hospital, Saolta University Health Care Group, and a Senior Lecturer at the University of Galway. He developed the MSc Applied Clinical Data Analytics program and holds a PhD from the Health Research Board (HRB) Clinical Research Facility Galway. Prior to medicine, he trained as an electronic and computer engineer, combining his expertise in data analytics with clinical practice. His research focuses on AI in healthcare, hypertension management, and clinical decision support systems. He was awarded a €700,000 grant from the HRB’s Clinician Scientist Fellowships (CSF 2023) for his project on AI-driven blood pressure treatment. Education: PhD in HRB Clinical Research Facility Galway Electronic and Computer Engineering training Research Interests: Integration of AI in clinical workflows Data-driven approaches to nephrology and cardiology Development of closed-loop drug delivery systems Grants & Awards: HRB Clinician Scientist Fellowship (2023) Labs/Teams: Leads initiatives in clinical data analytics and AI-driven healthcare solutions at University of Galway.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Amena Mahmoud is an academic researcher affiliated with Kafrelsheikh University, Faculty of Computers and Information, Egypt, where she has been actively contributing since 2020. Her work bridges computer science, artificial intelligence, and healthcare technologies, with a strong emphasis on medical imaging and diagnostic systems. Her research focuses on developing advanced deep learning models for medical applications such as brain tumor classification and AI-driven detection of diseases like COVID-19 from lung CT scans. She also explores machine learning optimization techniques for clinical decision support in neurosurgery and applies IoT-based solutions to environmental challenges including air pollution and smart traffic management. The publication trends indicate a strong focus on healthcare AI , medical image analysis , and intelligent systems with applications in both medicine and urban technology. Her work frequently appears in high-impact journals such as Expert Systems with Applications , PLoS ONE , and Symmetry . Scientific Sponsorship: World Bank Group U.S. Department of Homeland Security United States Agency for International Development Deanship of Scientific Research, King Faisal University Vetenskapsrådet Science and Technology Development Fund Bundesministerium für Bildung und Forschung She has secured funding from multiple international agencies, reflecting the global relevance of her research. Although no formal students are listed, her collaborative work suggests engagement in research teams. She has not received explicitly mentioned awards yet, but her publications demonstrate high academic influence and practical impact in public health and environmental sustainability.

Constantino Reyes-AldasoroView profile

Senior Lecturer

• Biomedical Image Analysis
• Medical Image Processing
• Computer Vision
• +12 more

Constantino Reyes-Aldasoro is a Senior Lecturer in Biomedical Image Analysis at the Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London. His research focuses on the analysis, interpretation, and visualization of biomedical data, particularly in the context of cancer, inflammation, and neurodegenerative diseases. PhD in Computer Science – University of Warwick (2004) MSc in Electrical Engineering – Imperial College London (1994) Bachelor’s in Mechanical and Electrical Engineering – Universidad Nacional Autónoma de México (1993) His research spans image analysis, machine learning, and computational modeling applied to biomedical imaging, with emphasis on electron microscopy, histopathology, and radiology. He has developed algorithms for cell segmentation, vessel tracing, and tumor microenvironment analysis, contributing significantly to open-source tools in the field. His recent publications show a strong trend toward integrating deep learning with traditional image analysis, especially in cancer diagnostics and Alzheimer’s disease assessment. He has also explored topological data analysis and persistent homology for evaluating dataset consistency in colorectal cancer research. Senior Member, IEEE Member Level 1, Sistema Nacional de Investigadores CONACYT (Mexico) He has supervised multiple PhD students in areas such as HeLa cell analysis, coronary plaque detection, and Alzheimer’s imaging. He has secured grants from the Leverhulme Trust, Australian Research Council, and Cancer Research UK. He is an academic editor for journals including PLOS ONE and Journal of Imaging , and has chaired conferences like MIUA and BMVA. He is part of the giCentre research group at City, and actively promotes interdisciplinary collaboration in AI for healthcare.

Jacob HerrmannView profile

Assistant Professor

• Biomedical device design & control
• Biomedical imaging and signal processing
• Computational modeling
• +3 more

Jacob Herrmann is an Assistant Professor in the Department of Biomedical Engineering at the University of Iowa College of Engineering. He joined the institution in 2022 and specializes in biomedical device design, respiratory dynamics, and computational modeling. His work focuses on mechanical ventilation optimization and pulmonary structural analysis. PhD in Biomedical Engineering from University of Iowa (2018) MS and BS in Biomedical Engineering from Boston University (2015 & 2012) His research integrates biomedical device control , multiscale imaging , and computational modeling to study ventilator-induced lung injury, pulmonary fibrosis biomechanics, and oxygenation optimization. Recent work explores multi-frequency oscillatory ventilation and AI-assisted CT imaging analysis. Key trends in his publications include: Development of computational models for lung mechanics Innovations in ventilator design and control systems Quantitative CT imaging applications in pulmonary injury Biomechanical feedback mechanisms in fibrosis Collagen network analysis in tissue failure Herrmann leads the Respiratory Dynamics Lab and maintains active memberships in the American Thoracic Society and Biomedical Engineering Society. His work addresses critical challenges in respiratory care through interdisciplinary engineering approaches.

Reza RamezanView profile

Associate Professor

• Spatial Statistics
• Environmental Statistics
• Biostatistics
• +2 more

Reza Ramezan serves as an Associate Professor in the Teaching Stream within the Department of Statistics at the University of Waterloo. His office is located in room M3 3107, with contact details including email rramezan@uwaterloo.ca and phone extension 48078. He is actively engaged in academic duties under the university's Teaching Stream faculty framework. Professor Ramezan's research demonstrates exceptional interdisciplinary breadth, anchored in Spatial Statistics and Extreme Value Theory . His work develops advanced methodologies for spatial extreme value models (e.g., SpatialGEV package) applied to environmental reconstruction through lake sediment analysis, while simultaneously bridging into Biostatistics for aging studies and Neuroscience Statistics for neural spike train modeling. This unique synthesis positions him at the intersection of statistical theory and high-impact environmental/health applications. Analysis of his 2022-2024 publications reveals three dominant research trajectories: (1) Paleoclimatic reconstruction using California lake sediments to model Holocene hydroclimatic variability and precipitation dipole patterns; (2) Development of computationally efficient Bayesian frameworks for spatial extreme value analysis; (3) Biostatistical investigations into cognitive aging, adiposity relationships, and neural data modeling. These threads consistently emphasize methodological innovation for complex spatiotemporal data. Scientific awards: No awards or fellowships are documented in the available information. Professor Ramezan participates in graduate education through examination committee service, as evidenced by 2022 committee membership records. While specific student advisees and grant funding details remain unreported in current sources, his publication record indicates active collaboration across environmental science, neuroscience, and public health domains. His methodological contributions suggest significant grant-supported research activity despite the Teaching Stream appointment.

Judith BruchfeldView profile

Associate Professor

• Infectious Diseases
• Tuberculosis
• Multidrug-Resistant Tuberculosis
• +11 more

Judith Bruchfeld is a docent in Infectious Diseases and Chief Physician at Karolinska Institutet’s Department of Medicine, Solna, and Karolinska University Hospital. She leads a clinical research group focused on tuberculosis and long-term effects of COVID-19, and has established a multidisciplinary long-COVID clinic to assess patients with persistent symptoms. Her research interests include: Multidrug-resistant tuberculosis (MDR-TB) treatment optimization Pharmacokinetics and drug exposure in TB Long-term sequelae of SARS-CoV-2 infection (PASC) Postural orthostatic tachycardia syndrome (POTS) Microvascular and cardiac dysfunction post-COVID Global TB control and diagnostics Immune responses in TB and post-viral syndromes Her recent publications highlight a strong focus on long-COVID, particularly cardiovascular and autonomic complications, using biomarkers, imaging, and interventional therapies. She also continues to publish extensively on TB, especially drug resistance, dosing, and treatment outcomes in global cohorts. Her work combines clinical research, epidemiology, and translational science. She has contributed to WHO guidelines on latent TB management and is involved in international collaborations on TB and long-COVID. She supervises researchers and leads studies on diagnostic pathways, stigma, and quality of life in TB and post-COVID patients. Her research is supported by grants, including a SEK 19 million award for long-COVID research. She is active in public outreach, media engagement, and advocacy for gender equality in medicine.

Dan MaView profile

Associate Professor

• Magnetic Resonance Imaging (MRI)
• Magnetic Resonance Fingerprinting (MRF)
• Medical Imaging Physics
• +3 more

Dan Ma is an Associate Professor of Neurosurgery and Biomedical Engineering at Duke University School of Medicine, with a focus on advancing quantitative Magnetic Resonance Imaging (MRI) technology. His research bridges MR physics, signal modeling, artificial intelligence, and clinical translation to improve disease diagnosis and treatment. Education: Ph.D., Case Western Reserve University School of Medicine (2015) His lab specializes in Magnetic Resonance Fingerprinting (MRF), a non-invasive technique for quantifying tissue properties, and develops multi-parametric MR methods for clinical applications in epilepsy, brain tumors, breast cancer, and prostate cancer. Recent work includes technical innovations in SyntheticMR implementation for radiation therapy and 3D MRF for dynamic contrast-enhanced imaging. Scientific Awards: Senior Member, National Academy of Inventors (2023) Junior Fellow, ISMRM (2017) I.I. Rabi Young Investigator Award, ISMRM (2016) Dan Ma is a core member of Duke's Center for Brain Imaging and Analysis. His research integrates AI into MRI post-processing and explores motion-robust scans for neonatal imaging. Publications highlight his contributions to pulse sequence design, relaxometry standardization, and cortical dysplasia characterization.

Greeshma AgasthyaView profile

Assistant Professor

• Medical Physics
• Biomedical Engineering
• Radiation Dosimetry
• +8 more

Greeshma Agasthya is an Assistant Professor at the Nuclear & Radiological Engineering and Medical Physics Program within the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Computational Medical Physics Laboratory and holds an adjunct appointment in the Radiation Oncology Department at Emory University . Doctorate in Biomedical Engineering from Duke University Postdoctoral training at Emory University's Winship Cancer Institute Her research focuses on multiscale digital twins for personalized radiation dosimetry, AI frameworks for cancer surveillance, and computational modeling of radiation protocols. Key areas include: Radiation dosimetry for imaging, therapy, and theranostics Medical imaging systems (neutron imaging, CT, tomosynthesis) Machine learning for clinical decision support Interdisciplinary collaboration across medical physics, radiology, and computer engineering Recent publications highlight her work on: Machine learning models for pediatric anxiety detection Temporal semantic drift in clinical terminology Dosimetric impacts of radiopharmaceutical extravasation Environmental exposure analysis for lung cancer risk Chromosome conformation-based radiosensitivity modeling She secured a DOE-BER grant for "Bridging the gap between low dose exposures and emergent physiology" and co-founded a Woodruff Innovation Nexus seed grant for multi organ-on-a-chip development in radiopharmaceutical therapy.

Hugues TalbotView profile

Researcher

• Computer Vision
• Machine Learning
• Image Segmentation
• +5 more

Hugues Talbot is a researcher affiliated with the Digital Vision Center, specializing in computer vision, machine learning, and optimization. His work spans medical imaging, digital pathology, and mathematical morphology, with recent publications focusing on deep learning applications for tumor segmentation, image restoration, and medical diagnostics. Research Interests: Computer Vision (e.g., image segmentation, mathematical morphology), Machine Learning (e.g., generative models, multiple instance learning), Medical Imaging (e.g., CT/MRI analysis, tumor detection), and Optimization (e.g., maximal flows, convex modeling). Key Projects: Development of algorithms for automated lymphoma prognosis using PET images, deep learning models for breast cancer diagnosis from cytology slides, and physics-informed neural networks for myocardial perfusion simulation. Technical Contributions: Hybrid heart segmentation algorithms, noise density analysis for image splicing detection, and discrete calculus methods for inverse problems in imaging. His work often integrates theoretical mathematical models (e.g., Ambrosio-Tortorelli functional) with practical applications in clinical settings, such as sacroiliitis detection, bone density estimation, and zebrafish brain volumetric analysis.

Frans VosView profile

Associate Professor

• Medical Image Analysis
• Quantitative MR Imaging
• Imaging Biomarkers
• +3 more

Frans Vos is an Associate Professor at the Department of Imaging Physics (Faculty of Applied Sciences) at Delft University of Technology, with a joint appointment at the Department of Radiology at ErasmusMC. His academic background combines Computer Science and Medical Informatics (University of Amsterdam, 1993) with a PhD in corneal shape measurement techniques (Vrije Universiteit Amsterdam, 1998). Joint faculty: TU Delft & ErasmusMC Specialization: Medical Image Analysis Key contributions: Imaging biomarkers, AI algorithms, and physics-informed modeling His research focuses on improving medical imaging for disease prevention and therapy optimization, particularly through integration of imaging physics with clinical applications. He led the FP7 VIGOR++ project (2016 completion) on Crohn's disease MRI assessment. The 2023-2024 publications reveal ongoing work in glioma characterization, radiology AI implementation frameworks, and 3D ultrasound embryo staging. Over his career, he has supervised 20 PhD students and 60 undergraduates in medical image processing. Current affiliations include the Imaging Physics (ImPhys) department at TU Delft and Radiology at ErasmusMC, with lab infrastructure and clinical translation as key focus areas.

Anja C. RodenView profile

Professor

• Thoracic Pathology
• Surgical Pathology
• Mediastinal Tumors
• +5 more

Anja C. Roden, MD is a Professor at Mayo Clinic College of Medicine , Department of Laboratory Medicine and Pathology, Anatomic Pathology. Board certified in anatomic and clinical pathology, she specializes in thoracic pathology with expertise in mediastinal tumors, lung tumors, malignant mesothelioma, interstitial lung diseases, and pulmonary transplant pathology. Clinical Roles : Medical Director, Immunostains Laboratory Research : 190+ peer-reviewed manuscripts, 40+ book chapters, Co-editor of "Mediastinal Lesions" and "Pulmonary Pathology" atlases Leadership : Past President, International Thymic Malignancy Interest Group; Current Chair, Thymic Tumor Subgroup, IASLC; Vice-Chair, European Society of Pathology Education : 2009: Surgical Pathology Fellowship, Mayo Clinic 2008: Anatomic & Clinical Pathology Residency, Mayo Clinic 1992: Medical Degree, Technical University of Dresden Research Focus spans mediastinal neoplasms, malignant mesotheliomas, biomarker development (diagnostic/predictive/theranostic), interstitial lung diseases, and pulmonary transplant pathology. Recent work emphasizes AI integration in ILD diagnosis, direct patient-pathologist interactions in transplant medicine, and molecular characterization of thoracic tumors. Scientific Awards : 2017: President's Pulmonary Pathology Society Innovation Award 2020: Best Author Award, Pathology Outlines Professional Activities : Active in clinical guideline development (WHO thoracic tumors 2021), serves on multiple international committees including the European Lung Cancer Congress Program Committee, Pulmonary Vascular Disease Steering Committee, and International Mesothelioma Panel. Regularly contributes to peer-review processes across major journals.

• Medical Imaging
• Machine Learning
• Real-Time Computing
• +7 more

Professor Bernhard Kainz is a leading academic in the Department of Computing at Imperial College London and Professor at Friedrich-Alexander-University Erlangen-Nuremberg. He heads the Image Data Exploration and Analysis Lab (IDEA Lab) and co-leads the Biomedical Image Analysis (BioMedIA) group , focusing on human-in-the-loop computing for healthcare applications. His research develops intelligent algorithms for multi-modal healthcare , emphasizing generative and discriminative machine learning methods to enhance diagnostic decision-making and provide real-time guidance during medical procedures. Current projects address democratizing rare healthcare expertise through AI, normative learning for human-like data analysis, and interpretable medical machine learning. Co-founder of Fraiya Ltd. for medical AI solutions Scientific advisor to ThinkSono Ltd. Leader in EPSRC Centre for Doctoral Training in Smart Medical Imaging and UKRI AI for Healthcare CDT He has received numerous accolades including MICCAI best paper awards, IEEE TMI Distinguished Reviewer status, and Imperial President’s Research Team Award. His team's work spans fetal imaging , cardiac ultrasound , digital pathology , and AI explainability in healthcare.

Rob JanssenView profile

Professor

• Value-Based Health Care
• Orthopedic Surgery
• Pediatric ACL Injuries
• +5 more

Prof. Dr. Rob Janssen is a lecturer in Value-Based Health Care at Fontys University of Applied Sciences and a part-time professor at Eindhoven University of Technology. He is an orthopedic surgeon specializing in knee disorders at Máxima Medical Center, Eindhoven/Veldhoven, and serves as course director for international orthopedic training programs. His work bridges clinical practice, research, and education, focusing on measurable value-driven healthcare and interprofessional collaboration. Dr. Janssen’s research interests span Value-Based Health Care implementation Pediatric and adult ACL injury management Biomechanical analysis of knee structures Cartilage regeneration techniques Digital health applications Interprofessional collaboration in healthcare His recent publications highlight trends in graft failure, organoid technology, and wearable sensor systems for knee instability. He contributes to national guidelines for knee care and collaborates with institutions such as Maastricht University, Aarhus University, and the ESSKA Pediatric ACL Monitoring Initiative (PAMI). His role as a residency instructor and guest lecturer underscores his commitment to medical education and postgraduate training.

Anca UdristoiuView profile

Associate Professor

• Artificial Intelligence
• Medical Imaging
• Deep Learning
• +4 more

Dr. Anca Udristoiu is an Associate Professor at the Department of Computers and Information Technology, University of Craiova's Faculty of Automatic Control, Computers and Electronics. She graduated from the same faculty in 2003 as a Software Engineering specialist and has been a PhD candidate in Computer Science since 2009 under Prof. Mircea Petrescu. Research Focus: Her work centers on AI-driven medical diagnostics, with emphasis on: Real-time analysis of endoscopic ultrasound (EUS) for pancreatic/liver pathologies Deep learning for cancer detection in biopsy samples Robotic navigation systems for minimally invasive procedures Computer-aided diagnosis frameworks for thyroid/liver disorders Teaching: Leads courses in Databases, Machine Learning, and Web Systems Engineering. Research Trends: Publications (2022-2025) demonstrate: Dominant focus on AI applications in endoscopic imaging (76% of recent works) Progression from theoretical models to clinical validation studies Integration of convolutional/long short-term memory neural networks Robotics development for endovascular/pulmonary interventions

Stuti ShroffView profile

Assistant Professor

• Gastrointestinal Pathology
• Molecular Pathology
• Pancreatic Cancer
• +4 more

Stuti Shroff, MBBS, PhD, is an Assistant Professor of Pathology at Harvard Medical School and Assistant Pathologist in Anatomic and Molecular Pathology at Massachusetts General Hospital. Her clinical expertise spans gastrointestinal (GI) pathology, head and neck pathology, liver pathology, molecular pathology, and pancreatic cancer, with multilingual proficiency in Gujarati, Hindi, and Marathi. Education & Training: MBBS: Padmashree Dr. D.Y. Patil Medical College PhD: University of Illinois Chicago Residency: State University of New York at Buffalo Fellowship: MD Anderson Cancer Center (dual fellowships) Research Focus: Dr. Shroff investigates molecular alterations in GI/pancreaticobiliary neoplasia and immune-mediated GI injury. Her work bridges translational pathology with clinical oncology, emphasizing biomarker discovery and diagnostic refinement for gastrointestinal cancers. Key projects include profiling neoplastic proliferations and immune-related adverse events in the GI tract. Publication Trends: Her recent articles (2018–2025) demonstrate a focus on molecular mechanisms in gastrointestinal and hepatic malignancies, with recurring themes in cancer signaling pathways (e.g., Notch), tumor microenvironments, and diagnostic innovations. Studies frequently incorporate genomic, transcriptomic, and imaging approaches to advance precision oncology.