Find Academic Supervisors

Doran S. MixView profile

Associate Professor

• Vascular Surgery
• Aortic Aneurysms
• Minimally Invasive Surgery
• +4 more

Doran S. Mix, M.D., is an Associate Professor of Vascular Surgery at the University of Rochester School of Medicine and Dentistry and holds a joint appointment as Research Assistant Professor at Rochester Institute of Technology. He serves as Director of Research for the UR Medicine Division of Vascular Surgery and leads the Cardiovascular Engineering Lab, focusing on abdominal aortic aneurysm (AAA) research through ultrasound imaging and 3D printing. His clinical expertise includes complex minimally invasive vascular procedures, aortic aneurysms, carotid artery stenting, and infected arterial grafts. Education: Bachelor's in Computer Engineering from Rochester Institute of Technology (RIT), followed by an M.D. from University of Rochester School of Medicine with Distinction in Research. Completed vascular surgery residency and an endovascular neurosurgery fellowship at Mayo Clinic. Research: The Cardiovascular Engineering Lab develops novel ultrasound techniques and 3D-printed models to assess AAA rupture risk and optimize patient-specific treatments. Collaborators include experts in biomedical engineering, echocardiography, and surgical outcomes analysis. Key projects include predicting aneurysm growth via strain analysis and improving endovascular repair techniques. Awards: Over 20 honors including the Paul Schloerb Award for Critical Care, Arnold P. Gold Humanism Award, and multiple research fellowships. Active in national vascular surgery conferences and peer-reviewed publications. Labs/Teams: Leads the Cardiovascular Engineering Lab with co-investigators Michael Richards (RIT), Karl Schwarz (echocardiography), and Michael Stoner (vascular surgery chief). Collaborates with interdisciplinary teams on translational cardiovascular research.

Zhan ZhangView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Computer-Supported Cooperative Work (CSCW)
• Wearable Technology
• +6 more

Zhan Zhang is an Associate Professor at the Seidenberg School of Computer Science and Information Systems at Pace University , where he directs the Smart Health Lab . His research focuses on Human-Computer Interaction , Wearable Technology , and Healthcare Informatics , with an emphasis on Emergency Medical Services and Patient Engagement . Education: PhD in Information Science (Drexel University, 2016), MS in Information Management (Syracuse University, 2011), BS in Management Information System (Beijing University of Posts and Telecommunications, 2009) Research Areas: Human-Computer Interaction, Computer-Supported Cooperative Work, Wearable Technology, Healthcare Informatics, Clinical Decision Support Systems, Human-AI Interaction His research explores designing novel technologies to support information collection, decision-making , and collaboration in healthcare settings . Key projects include smart glasses for emergency care , patient-facing health data applications , and AI-powered clinical decision support systems . Publications span 15 recent articles in venues like JMIR , CHI , and JAMIA Open , covering topics from touchless interaction to AI explanations in healthcare . Scientific Awards: NIH R15 Grant (2024) AHRQ R21/R33 Grant (2024) Kenan Award for Teaching Excellence (2023) NSF CAREER Award (2023) Pace University Scholarly Research Grants (multiple) Wilson Faculty Fellowship (2019) He has advised numerous PhD and Master’s students like Enze Bai and Le Zhou , and his work has been supported by over $1.6 million in federal grants from NSF, NIH, and AHRQ.

Fredrik StrandView profile

Researcher

• Artificial Intelligence
• Medical Imaging
• Breast Cancer Research
• +3 more

Fredrik Strand is a researcher at the Karolinska Institute , specifically within the Department of Oncology-Pathology under the Faculty of Medicine . He leads the Computational Breast Imaging Group , focusing on leveraging Artificial Intelligence (AI) for early breast cancer detection , precision diagnostics , and image-guided minimally invasive procedures . His work spans cross-disciplinary collaboration with institutions like the Royal Institute of Technology (KTH) and involves major clinical trials such as ScreenTrustCAD and ScreenTrustMRI . His research emphasizes AI in precision screening to address interval cancers and optimize radiologist workflows. Projects like VAI-B and RadioVal aim to validate AI algorithms and predict therapy response for neoadjuvant treatment . Recent publications highlight advancements in multi-modality imaging (mammography, MRI), GAN-based synthetic data generation , and trustworthy AI frameworks for clinical deployment. A consistent focus is on cost-effectiveness , equity in AI diagnostics , and human-AI interaction dynamics . Key trends in his work include deep learning for tumor detection , reinforcement learning for screening policies , and multi-institutional validation of AI tools. His research is funded by organizations such as Medtechlabs , Vinnova , EU Horizon Europe , and the Swedish Breast Cancer Association .

• Nuclear Medicine Physics
• Medical Imaging
• SPECT/CT Image Reconstruction
• +3 more

Michael A. King is a Professor in the Department of Radiology at UMass Chan Medical School and holds an appointment in Biochemistry and Molecular Biotechnology at Morningside Graduate School of Biomedical Sciences. His work focuses on nuclear medicine physics and advanced SPECT/CT imaging systems. UMass Chan Medical School, Worcester, MA T.H. Chan School of Medicine Morningside Graduate School of Biomedical Sciences Research Interests: Dr. King specializes in correcting image degradation factors in nuclear medicine, including attenuation, scatter, and motion artifacts . His lab develops deep learning algorithms for cardiac and brain imaging, adaptive collimator designs for SPECT systems, and task-based image quality assessment using human and numerical observers. Recent Research Trends include respiratory motion compensation in cardiac SPECT, multi-pinhole collimator optimization , and AI-driven dose reduction techniques. His team’s work on dynamic SPECT imaging enables non-invasive quantification of physiological functions. Scientific Contributions: Recipient of three NIH R01 grants (R01-EB022092, R01-EB022521, R01-HL154687) Co-investigator on grants exploring wave-radar motion estimation and pediatric SPECT optimization Pioneered motion-corrected reconstruction algorithms integrated into clinical workflows Teaching: Dr. King leads the Physics of Radiology for Residents course and prepares students for the American Board of Radiology physics exam through Physics Review Lectures . He has mentored graduate students like Anne-Dorte Achtert and Daniel deVries, whose work received Best Scientific Paper Awards from the Journal of Nuclear Cardiology and Journal of Nuclear Medicine.

• Medical Imaging
• Computer Science
• Biophysics
• +6 more

Clifford Grant Lindsay is an Assistant Professor at UMass Chan Medical School's T.H. Chan School of Medicine, Department of Radiology. He holds a BS in Computer Science from University of California, San Diego and a PhD in Computer Science from Worcester Polytechnic Institute. NIH/NIBIB K25EB019032-01A1 Career Award (2015-2019) Co-investigator on WPI/UMass grant ARMO phantom development (2015-2017) NIH Clinical Research Grant (2016-2018) for marker-less motion tracking His research focuses on medical imaging physics, particularly motion correction in SPECT/PET imaging through computational modeling and robotic phantoms. Key contributions include: Deep learning for attenuation map estimation in cardiac imaging Development of articulated robotic motion phantoms Quantitative MRI volumetric analysis in GM1 gangliosidosis Respiratory motion compensation techniques Surface marker correlation for hybrid imaging systems 4D phantom modeling for emission tomography Publications span Medical Imaging , Neurology , and Biophysics , with recent work on machine learning applications and neurological disorder imaging. Collaborations include Michael King and Mohammed Shazeeb on motion tracking solutions for nuclear medicine.

• Computational Fluid Dynamics
• Biomedical Engineering
• Cerebral Aneurysms
• +5 more

Iolanda Raquel Fernandes Velho is a researcher at Universidade Lusófona, specializing in computational and biomedical engineering with a focus on cerebral aneurysms and medical imaging. She holds a PhD in Computational Engineering and an integrated Master's in Biomedical Engineering, reflecting her strong interdisciplinary background. PhD in Computational Engineering Integrated Master in Biomedical Engineering Her research interests lie at the intersection of computational modeling and clinical neuroscience. She applies computational fluid dynamics (CFD) to analyze hemodynamic behavior in cerebral aneurysms, aiming to improve diagnostic and treatment strategies. Her work also involves MRI-based optimization of hippocampal volumetric treatments, contributing to advances in neuroimaging and personalized medicine. The recent publications demonstrate a consistent focus on patient-specific modeling of cerebrovascular dynamics. Her work integrates medical imaging with advanced simulation techniques to study near-wall flow phenomena and hemodynamic parameters in brain aneurysms, primarily using CFD methodologies. While no formal scientific awards are listed, her research output in specialized journals indicates active scholarly contribution to biomedical engineering and neuroradiology. She has contributed to research on examination portability and clinical imaging workflows, suggesting engagement with broader healthcare system challenges. No information is available about student supervision or research grants. Iolanda is associated with research platforms like PURE, indicating institutional research activity, though no specific lab or research team is mentioned in the provided text.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +4 more

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +3 more

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +3 more

Prof. Jérôme Schmid is a Full Professor (Professeur HES ordinaire) at the Haute école de santé - Genève, part of the Faculty of Health. His expertise spans medical image processing, artificial intelligence (AI), and their clinical applications. He leads innovative projects addressing challenges in diagnostics, surgery planning, and medical education. Key roles include Principal Investigator in grants funded by Swiss National Science Foundation, Swiss Innovation Agency, and others. Research Focus: Combines AI with medical imaging for applications such as Parkinson’s disease detection via SPECT, breast lesion analysis using ultrafast MRI, and AI-driven radiography training tools. Projects emphasize interdisciplinary collaboration with hospitals and industry partners. Projects: DeepDAT (2022–2025): AI for Parkinson’s diagnosis via SPECT imaging. SUBREAM (2022–2025): Rapid breast MRI protocols with AI integration. AIRx (2019–2020): AI-based radiography simulation for student training. MyHip (2012–2014): Patient-specific hip arthroplasty planning. Publications: Focus on AI-driven diagnostics, imaging techniques, and medical education. Recent works address drowning detection via post-mortem CT, multimodal AI fusion for breast cancer, and serious games in radiology training. Grants & Partnerships: Swiss National Science Foundation: FAI analysis via multi-modal imaging. Innosuisse: Low-cost X-ray detectors for developing countries (GlobalDiagnostiX). Swiss Cancer Research Foundation: Breast MRI advancements (SUBREAM). Labs/Teams: Collaborates with the Geneva University Hospitals, EPFL, and industry (e.g., Medacta International SA) on hardware and clinical AI solutions.

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +5 more

Prof. Jérôme Schmid is a Professor at the Geneva School of Health Sciences (Haute école de santé - Genève) in the Department of Medical Radiology Techniques. He specializes in medical image processing, artificial intelligence applications in healthcare, and computer-aided diagnosis systems. His work focuses on improving diagnostic accuracy through machine learning, deformable models, and multimodal imaging fusion. Education includes a Master of Science (MSc) in Health Sciences from HES-SO/UNIL and a Bachelor of Science (BSc) in Medical Radiology Techniques from HES-SO. He also contributed to advanced education programs in 3D technologies in orthopedics and sports medicine through collaborations with the Geneva University Medical Faculty. Research interests revolve around AI-driven medical imaging analysis, specifically in breast lesion detection via MRI, Parkinson's disease diagnosis through SPECT imaging, and fracture recognition in radiographs. He leads projects addressing global healthcare challenges such as affordable imaging systems for low-resource countries and AI integration into radiology education through platforms like AIRx and ImmeRgaMe. Current projects (2022-2025) include DeepDAT (AI for Parkinson's diagnosis using SPECT) and SUBREAM (ultrafast breast MRI protocols). Completed projects span hip arthroplasty planning (MyHip), thermal-tolerant X-ray detectors, and FAI impingement modeling. His work is funded by Swiss National Science Foundation, Innosuisse, and private sector partnerships. Publications span 2015-2025, focusing on image segmentation, AI integration in diagnosis, and medical education innovations. He collaborates with academic partners like Hôpitaux Universitaires de Genève and industry leaders such as Medacta International.

Mark AllenbyView profile

Senior Lecturer

• Tissue Engineering
• Biomedical Image Analysis
• Computational Biology
• +5 more

Dr. Mark C Allenby is a Senior Lecturer in Biomedical Engineering at the University of Queensland's School of Chemical Engineering. He leads the BioMimetic Systems Engineering (BMSE) Lab, focusing on tissue engineering, bioreactor design, and computational modeling. His research integrates biomedical image analysis, computational biology, and vascular systems engineering, with applications in cardiovascular disease, cell therapy manufacturing, and diagnostic models for transplant-associated complications. Allenby has secured over $3.8M in competitive grants, including the Heart Foundation Future Leader Fellowship (2025-2029) and ARC DECRA Fellowship (2022-2025). He holds adjunct roles at QUT and chairs professional societies like ASBTE. Education: PhD and MSc in Chemical Engineering, Imperial College London (UK) Bachelor's degrees in Mathematics and Chemistry, Pepperdine University (USA) Research Themes: Vascularized Immune Models: Predicting transplant rejection through blood-derived cell cultures Cell Therapy Manufacturing: Reducing costs via bone marrow-inspired bioreactors 3D Biomanufacturing: High-content screening for drug discovery and vascular disease modeling Patient-Specific Models: Additive manufacturing for aneurysm rupture risk prediction Grants & Awards: $3.8M in 25 competitive grants (2015–2024) 2024 UQ Foundation Research Excellence Award 2023 TERMIS-AP Young Investigator Award Labs & Teams: BMSE Lab (UQ), collaborations with Royal Brisbane & Women's Hospital, Queensland Cord Blood Bank, and Technical University of Munich. Active in industry partnerships via Advance Queensland and Bionics Gamechangers Australia.

Tim RosenowView profile

Research Fellow

• Medical Physics
• MRI
• Preclinical MRI
• +7 more

Dr. Tim Rosenow is a Senior Research Fellow at the Centre for Microscopy, Characterisation & Analysis and an Adjunct Senior Research Fellow at the School of Human Sciences at The University of Western Australia. He serves as the Facility Fellow for the 9.4T preclinical MRI at the National Imaging Facility, University of Western Australia node. His work bridges physics, medical imaging, and clinical applications with a focus on developing novel imaging techniques. Education: PhD in Paediatrics, 'Quantitative analysis of chest computed tomography in early cystic fibrosis lung disease' (2017) Graduate Certificate in Paediatric Respiratory Science (2011) Bachelor of Science in Physics/Computer Science (2007) Dr. Rosenow's research centers on preclinical MRI and medical image analysis techniques. He has designed, validated, and patented methods for disease quantification in respiratory conditions. His current work focuses on novel MRI techniques to measure physiological processes in health and disease. His research spans biomedical engineering, physics, and clinical medicine, with applications in cystic fibrosis, brain imaging, and disease modeling. Analysis of his recent publications reveals a strong focus on advanced imaging techniques across multiple domains. His work ranges from preclinical MRI development to clinical applications in cystic fibrosis and neuroscience. The publications demonstrate expertise in both technical MRI development and clinical applications, with collaborative work spanning multiple institutions globally. Dr. Rosenow is currently an investigator on an active research project 'Amplifying the response to radiotherapy for children with aggressive brain cancer' (2024-2027), funded by the Department of Health (Western Australia). He works with the Bruker BioSpec 94/30 US/R MRI equipment at the Centre for Microscopy, Characterisation & Analysis, focusing on applications in magnetic resonance imaging, nuclear magnetic resonance imaging, and in vivo studies.

Declan O'ReganView profile

Professor

• Artificial Intelligence in Healthcare
• Cardiovascular Disease Mechanisms
• Medical Imaging
• +4 more

Declan O'Regan is the British Heart Foundation Chair of Cardiovascular AI and Professor of Imaging Sciences at Imperial College London's Faculty of Medicine. He leads the Computational Cardiac Imaging Group at the MRC Laboratory of Medical Sciences and serves as Director of Imaging Research at Imperial College Healthcare NHS Trust. His research integrates machine learning with cardiovascular imaging, genetics, and environmental data to uncover disease mechanisms and improve predictive medicine. Key affiliations include the BHF Centre of Research Excellence, EPSRC Centre for Maths of Precision Healthcare, and multiple imaging networks. Education: BSc (University of London), MBBS (University of London), PhD (Imperial College London), and postgraduate training in Radiology (FRCR) and Medicine (FRCP). Professional roles extend to editorial boards of journals like the European Heart Journal–Cardiovascular Imaging and leadership in societies such as the British Society of Cardiac MRI. Research focuses on AI-driven diagnostics, cardiovascular aging, and genetic risk factors. Notable work includes developing algorithms for predicting survival from cardiac motion and collaborating with industry on drug discovery. Funders include the MRC, BHF, NIH, and pharmaceutical/biotech companies. Lab activities emphasize translational research, patient collaboration, and open science. His group’s innovations span cardiac motion capture, genotype-phenotype modeling, and AI-enhanced ECG analysis for early disease detection. Current initiatives explore the impacts of environmental factors (e.g., aircraft noise) on cardiovascular health and post-COVID-19 cardiac sequelae.

• Medical Imaging
• Machine Learning
• Computer Vision
• +3 more

Aggelos K Katsaggelos is a Professor at Northwestern University, holding roles in both the McCormick School of Engineering and the Department of Radiology (Basic and Translational Radiology Research). His research spans medical imaging, machine learning, computer vision, semiconductor materials, and astrophysics simulations. Key contributions include advancements in deep learning for medical diagnostics, semiconductor detector characterization, and computational astrophysics. He is affiliated with the Northwestern University Clinical and Translational Sciences Institute (NUCATS). Research interests include AI-driven medical imaging analysis, signal processing for healthcare applications, and semiconductor physics. Notable projects include work on POSYDON for binary star simulations and innovations in GAN-based image super-resolution. He has authored over 350 publications, focusing on interdisciplinary applications of machine learning in healthcare and engineering. No scientific awards are explicitly listed, but his extensive publication record reflects significant academic contributions. External professional relationships for 2024 were none, indicating focused academic engagement. Advising and grants details are not specified in the provided texts.

• Nuclear Engineering
• Materials Science
• Nuclear Materials
• +3 more

Lorenzo Vergari is an Assistant Professor in the Department of Nuclear, Plasma & Radiological Engineering at the University of Illinois. His research focuses on nuclear materials, particularly graphite in high-temperature environments, molten salts, and tritium management. He holds a B.S. from Politecnico di Milano (2016), M.S. degrees from Politecnico di Milano (2018) and Politecnico di Torino (2019), and a Ph.D. from UC Berkeley (2023). He is affiliated with the Advanced Blankets & Coolants Laboratory. His professional roles include membership in the ANS (2020–2024), ASME Task Group on Graphite Issues in MSRs (2022–present), and IAEA Working Party on Raman Characterization of Graphite (2023–present). He is a licensed Professional Engineer in Italy (2020). Research interests emphasize corrosion mechanisms, hydrogen behavior in graphite, and computational modeling for nuclear systems. His work addresses challenges in molten salt reactors, waste disposal, and fusion-relevant materials. Recent studies investigate surface fluorination effects, neutron activation impacts, and tritium desorption dynamics. Key achievements include the Young Researcher Award (2020), Berkeley Graduate Fellowship (2019), and recognition as an excellent teacher at UIUC (2024–2025). He teaches NPRE 330 and NPRE 498 NC3/NC4 courses, focusing on materials in nuclear engineering and nuclear chemical engineering.