Find Academic Supervisors

• Machine Learning in Surgery
• Cardiothoracic Surgery Outcomes
• Clinical Informatics
• +3 more

Chin Siang Ong is an Assistant Professor of Surgery (Surgical Outcomes) at Yale University School of Medicine. He holds an MBBS from National University of Singapore (2010), PhD from Johns Hopkins University (2019), and MPH from Harvard University (2025). His research focuses on integrating machine learning with surgical outcomes, particularly in cardiac surgery and critical care. Key areas include developing AI-driven decision support systems, predicting postoperative complications, and optimizing healthcare workflows. He collaborates on projects such as SurgeryLLM, a large language model framework for surgical decision-making, and studies clinical variables influencing readmissions and mortality. His work addresses challenges in ECMO management, fetal surgery simulation, and healthcare policy impacts on TAVR utilization. Education: MBBS (2010, NUS), Residency (General Surgery 2011, Cardiothoracic Surgery 2015 at National University Health System), Research Fellowships at Johns Hopkins Hospital and Massachusetts General Hospital (2020–2021). Research Interests: Machine learning applications in surgery, surgical outcomes analysis, cardiovascular device development, and translational clinical informatics. His lab explores causal inference methods, predictive modeling for readmissions, and biomaterials for vascular grafts. Publications span machine learning in appendicitis diagnosis, cardiac surgery bleeding management, and macrophage roles in atrial fibrillation. He contributes to editorial work on AI in physiology and collaborates with institutions like the Stephen & Denise Adams Center for Parkinson’s Disease Research at Yale.

Ming MaView profile

Assistant Professor

• Artificial Intelligence
• Medical Imaging
• Large Language Model
• +3 more

Ming Ma is an Assistant Professor in the Department of Graduate Computer Science and Engineering at the Katz School of Science and Health, Yeshiva University. He leads the Medical Imaging and Artificial Intelligence lab. He holds a Ph.D. in Computer Science from Stony Brook University and completed a postdoctoral fellowship at Stanford University. His research focuses on integrating artificial intelligence with medical imaging, particularly in areas like medical image segmentation, deep learning applications in healthcare, and geometric modeling for biomedical systems. Education: Ph.D. in Computer Science, Stony Brook University, USA Postdoctoral Scholar, Stanford University, USA Research Interests: His work spans Artificial Intelligence , Medical Imaging , Large Language Models , Geometric Modeling , and Computer Vision . He develops novel algorithms for medical image analysis, including techniques for dose prediction in radiation therapy, pulmonary nodule detection, and segmentation of vascular structures. Recent projects emphasize the application of graph convolutional networks and optimal transport methods in low-dose CT imaging. Conference Contributions: He serves on program committees for conferences like AAAI 2022, ICONIP 2024, and CHASE 2025, reflecting his leadership in advancing AI and biomedical engineering research. Labs/Teams: His lab at Yeshiva University focuses on interdisciplinary research at the intersection of AI, medical imaging, and computational anatomy, with a commitment to translating algorithmic innovations into clinical applications.

• Cardiology
• Cardiovascular Science
• Ischemic Heart Disease
• +6 more

Colin Berry is a Professor of Cardiology and Imaging at the University of Glasgow. He holds leadership roles as Director of the British Heart Foundation (BHF) Centre of Research Excellence, Director of Research in the School of Cardiovascular and Metabolic Health, and Director of Research at the Golden Jubilee National Hospital. Additionally, he serves as a Consultant Cardiologist at both the Golden Jubilee and Queen Elizabeth University Hospitals in Glasgow. His professional affiliations include Executive Editor of the European Heart Journal , President of the British Society of Cardiovascular Magnetic Resonance (2022–2024), and Steering Group membership in the Coronary Vasomotor Disorders International Study Group (COVADIS). He has founded specialized working parties and led major research initiatives within cardiovascular societies. Colin Berry earned his medical degree from the University of Glasgow (1987–1993) and a Maîtrise in Physiology from the University of Paris (1989–1990). He completed a PhD in cardiovascular science at the University of Glasgow in 2002. His clinical training in Cardiology and Internal Medicine spanned Glasgow (1993–2001) and the Montreal Heart Institute (2005–2006), supported by prestigious fellowships including the BHF International Fellowship and Royal Society of Edinburgh International Exchange Award. Post-doctoral studies at the NIH (2007–2009) and a Lord Kelvin Adam Smith Fellowship further enhanced his expertise in advanced cardiovascular imaging. He secured a Senior Fellowship from the Scottish Funding Council in 2009. His research focuses on Ischaemic heart disease (angina, heart attack, heart failure) Small vessel disease (cardiac and cerebral) Therapy development using MRI Sex disparities in cardiovascular care Post-COVID-19 multisystem consequences Precision medicine and stratified approaches Berry has pioneered groundbreaking technologies, such as transcatheter aortic valve replacement (TAVR) in the UK, and world-first MRI mapping of heart attack injuries in 2011. He leads large-scale trials like the CorMicA and PRIZE studies, advancing treatments for microvascular angina and heart failure. His scientific contributions include redefining international guidelines—from 'Syndrome X' to 'INOCA'—and co-authoring policy reports on Bias in Biology (BHF 2019) and the Igniting Change EDI strategy (BHF 2022). He advocates for healthcare equity via initiatives like the BHF Centre’s focus on sex, race, and deprivation disparities. Research grants and collaborations span the BHF, UKRI, and industry partners like Abbott and Siemens Healthineers. His teams investigate cardiac biomechanics, computational models (e.g., SofTMechMP with MIT), and long-term effects of infections like SARS-CoV-2 on cardiovascular systems. Berry also co-leads the UK COVID-HEART study and directs the CISCO-19 and CISCO-21 trials exploring post-COVID-19 cardiac and exercise interventions.

• Medical Imaging
• Radiography
• Radiation Protection
• +3 more

Dr. John William Robinson is a Lecturer in the Department of Medical Radiation Sciences at the University of Sydney. He is a member of the Sydney Southeast Asia Centre and the Image optimisation and perception group (MIOPeG). His research focuses on medical imaging quality, radiation exposure optimization, and clinical decision-making in radiography, particularly for obese patients and interventional radiology procedures. He has authored over 30 peer-reviewed publications, with recent work addressing image quality assessment between radiographers and radiologists, radiographer education, and radiation dose reduction strategies. Robinson's contributions span technical advancements in imaging systems (e.g., DigiBit exposure adjustment) and qualitative studies on clinical practice challenges. His research emphasizes bridging gaps between technological innovations and practical clinical workflows to enhance patient care and safety. Key research themes include optimizing radiation exposure in interventional radiology, improving diagnostic accuracy through advanced imaging techniques, and understanding radiographers' roles in modern healthcare systems. His work often integrates empirical studies with technological solutions to address real-world clinical needs. Notable projects include analyzing image quality metrics in uterine artery embolisation and investigating radiographers' decision-making for obese patients. His findings highlight the importance of standardized protocols and interdisciplinary collaboration in minimizing patient radiation exposure while maintaining diagnostic quality. Robinson has contributed to educational initiatives such as remote simulated learning in computed tomography and the development of reporting frameworks to reduce diagnostic errors. His research also extends to leadership and emotional intelligence among radiography professionals, emphasizing the psychosocial aspects of healthcare careers.

• Perioperative Dentistry
• Bone Mechanobiology
• Oral Microbiology
• +2 more

Dr. Hanako Suenaga is a Lecturer in Restorative and Reconstructive Dentistry at the University of Sydney's School of Dentistry, Faculty of Medicine and Health. She maintains clinical specialist registrations in Australia (AHPRA) and Japan while leading interdisciplinary research initiatives. As a member of the Charles Perkins Centre, she coordinates courses across Bachelor of Oral Health, Doctor of Dental Medicine (DMD), Doctor of Medicine (MD), and Doctor of Clinical Dentistry (DClinDent) programs, including leadership in DMD curriculum development. Her research focuses on two primary domains: Perioperative Dentistry examines oral-systemic health connections in surgical patients, investigating how oral microbiome influences postoperative complications and developing preoperative dental protocols to improve outcomes. Bone Mechanobiology explores jawbone responses to dental prosthetics through biomechanical modeling and advanced imaging (NaF-PET), analyzing pressure distribution and metabolic reactions to mechanical loading. Publications demonstrate consistent focus on dental biomechanics (45%), prosthetic design (30%), and surgical-dental integration (25%), with recent work emphasizing perioperative care innovations. Current work includes a 2025 RACDS-funded project investigating oral microbiome links to surgical outcomes. Dr. Suenaga leads an international multidisciplinary team at Westmead Hospital collaborating with oral microbiologists, engineers, and clinical researchers. Her laboratory employs finite element analysis, PET/CT imaging, and in vivo pressure measurement systems to advance translational dental care.

Rick FranichView profile

Professor

• Medical Physics
• Radiotherapy
• Radiation Dosimetry
• +5 more

Professor Rick Franich is a faculty member in the School of Science at RMIT University, specializing in Medical Physics and Radiotherapy. His research focuses on radiation dosimetry, radiation transport modeling, and applications in healthcare. He has collaborated with institutions like Peter MacCallum Cancer Centre and the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) on projects addressing radiation delivery accuracy, beam characterization, and in vivo dose assessment. Education: Details not explicitly provided in the text. Affiliations: RMIT University, RMIT Institute for Sustainable Health and Well-Being, IAEA advisory group. His research interests include dosimetric modeling in radiotherapy, Monte Carlo simulations for radiation transport, and ion beam analysis. He has secured grants such as the 2007 RMIT Emerging Researcher Grant and received teaching awards including the 1999 RMIT Physics Students Association Lecturer of the Year Award. Teaching roles include coordinating the Master of Applied Science (Medical and Health Physics) and courses in Medical Imaging Physics and Radiation Protection. He supervises PhD and Master’s students on projects related to radiation shielding, brachytherapy verification, and adaptive radiotherapy. Notable consultancy includes advising the International Atomic Energy Agency (IAEA) on improving heavy ion beam analytical techniques. His work spans clinical applications like proton therapy verification and space radiation modeling for astronaut safety. Scientific awards include recognition for teaching excellence and research contributions. His lab and team focus on advancing medical physics through interdisciplinary collaborations in oncology, radiation safety, and technology integration.

• Artificial Intelligence
• Medical Imaging
• Structural Engineering
• +5 more

Dr. Azadeh Alavi is a Lecturer at RMIT University's School of Computing Technologies in Melbourne, Australia. Her research focuses on interdisciplinary applications of artificial intelligence, particularly in medical imaging, structural engineering, quantum computing, and sustainability. She supervises PhD and master's projects in areas such as AI-driven medical image analysis, digital twin technology for infrastructure monitoring, and psychedelics-inspired therapeutics design. Her work spans diverse domains including healthcare analytics (e.g., diabetic foot ulcer classification, dementia prediction), civil infrastructure monitoring (e.g., bridge integrity assessment, real-time structural health systems), and theoretical AI (e.g., analyzing AI systems against consciousness definitions). Key contributions include developing PRIMITI for miRNA-mRNA interaction prediction and SellaMorph-Net for sella turcica segmentation in cephalometric imaging. Dr. Alavi’s recent publications highlight advancements in multimodal fusion learning for medical imaging, quantum-classical hybrid models for structural monitoring, and AI applications in fashion sustainability. She is an active collaborator in international research networks and maintains an ORCID profile (0000-0002-9565-217X). Her supervision portfolio includes projects on AI ethics, digital twin implementations, and health data mining. She teaches advanced computing modules and advises students on cutting-edge AI methodologies for real-world challenges across engineering and healthcare sectors.

Andrew GoertzenView profile

Adjunct Professor

• Nuclear Medicine
• Medical Imaging
• Biomedical Physics
• +3 more

Andrew Goertzen is an Adjunct Professor in the Department of Physics and Astronomy at the University of Manitoba, serving as Coordinator of the PET CT Program. His research focuses on advancing nuclear medicine technologies, particularly in PET imaging instrumentation, neurodegenerative disease biomarkers, and cardiovascular risk assessment. He leads interdisciplinary projects in medical physics, combining expertise in detector design, imaging algorithms, and clinical applications. Key research areas include: PET scanner optimization (dual-layer detectors, Monte Carlo modeling) Neuroimaging of Alzheimer's and Parkinson's disease Cardiac and metabolic disease diagnostics Bone fracture risk prediction via finite element analysis His work bridges basic physics with clinical translation, emphasizing innovations in hybrid imaging modalities (PET/MR) and radiation safety standards. Recent contributions include developing the CRAX cardiovascular risk tool and improving PET reconstruction algorithms using GPU computing.

Anders WåhlinView profile

Associate Professor

• Cerebral Hemodynamics
• Neuroimaging
• Aging Neuroscience
• +2 more

Anders Wåhlin is Associate Professor at Umeå universitet's Department of Applied Physics and Electronics, with additional affiliations in Biomedical Engineering and Functional Brain Imaging where he serves as Director of the Umeå Centre for Functional Brain Imaging (UFBI). His multidisciplinary research integrates neuroimaging, hemodynamics, and aging neuroscience. Research focuses on cerebrovascular physiology using advanced 4D flow MRI techniques to investigate cerebral blood flow regulation, vascular stiffness, and blood-brain barrier integrity in aging populations. Additional work explores neurochemical pathways linking dopamine receptor availability to cognitive decline. Publications demonstrate consistent emphasis on neurovascular coupling mechanisms, with methodological innovations in MRI quantification and computational fluid dynamics modeling. Recent work establishes connections between vascular pathology, neuroinflammation, and neurodegenerative processes. Leads multiple research groups in Biomedical Engineering and coordinates the MT4North project developing medical technology business models.

• Clinical Informatics
• Natural Language Processing
• Medical Data Mining
• +3 more

Pradeep Mutalik, MD, is an Associate Research Scientist in Psychiatry at Yale School of Medicine with over 35 years of experience. His work spans neurophysiology, clinical informatics, radiology, and medical data mining. Current research focuses on NLP for electronic medical record extraction at the VA Clinical Epidemiological Research Center. Research areas include clinical NLP, virtual trial emulation, medical informatics, and VA health data systems. He developed methods for negation detection in EMRs and validation techniques for clinical data extraction. Publications demonstrate a progression from early neuroscience and pain research (1980s) to radiology informatics (1990s) and contemporary clinical NLP applications for COVID-19 and diabetes research.

Suneil JainView profile

Clinical Professor

• Prostate Cancer
• Radiotherapy
• Radiation Therapy and Drug Combination Studies
• +4 more

Suneil Jain is a Clinical Professor at Queen's University Belfast's School of Medicine, Dentistry and Biomedical Sciences, with a focus on prostate cancer and advanced radiotherapy. He leads the Belfast Manchester FASTMAN Movember/Prostate Cancer UK Centre of Excellence and has expertise in hypofractionated radiotherapy, biomarkers, and nanotechnology. His research includes clinical trials, translational studies, and collaborations across disciplines. He has secured 12 awards, including fellowships and research distinctions. Jain actively supervises PhD students and publishes extensively on prostate cancer treatment outcomes, radiation toxicity, and therapeutic innovations. His work aligns with UN Sustainable Development Goals related to health and well-being.

Jari TurunenView profile

Lecturer

• Sensitivity Analysis
• Radionuclide Transport Modeling
• Environmental Safety Assessment
• +4 more

Jari Turunen is a University Lecturer in Computing Sciences at Tampere University. His expertise spans signal processing, environmental safety analysis, and machine learning applications. He holds a Doctor of Science (Technology) in Information Technology (2003) and a Vocational Teacher qualification (2016) from Tampere University of Applied Sciences. His research focuses on sensitivity analysis of radionuclide transport models, environmental risk assessment, and 3D data economy applications. Notable contributions include neural network surrogate models for geochemical process emulation and studies on post-glacial land uplift in Fennoscandia. Turunen has supervised multiple master's theses in data warehouse methodology and telecom revenue analysis. He actively reviews for journals like Journal of Urban Planning and Development and collaborates internationally on topics such as radioactive waste safety analysis and industrial data utilization.

Alex LennonView profile

Senior Lecturer

• Biomechanics
• Biomaterials
• Medical Device Design
• +4 more

Dr. Alex Lennon is a Senior Lecturer in the School of Mechanical and Aerospace Engineering at Queen's University Belfast. His research focuses on biomechanics, biomaterials, and mechanobiology, with applications in orthopaedic and cardiovascular interventions. He collaborates with institutions like Musgrave Park Hospital and international partners on projects such as orthopaedic biomechanics, biomaterials testing, and computational modelling. Teaching responsibilities include courses on Strength of Materials and Bio-solid Mechanics. Dr. Lennon has supervised 13 PhD students, including notable alumni like Dr. Rory O'Neill (University of Edinburgh) and Dr. Pavel Galibarov (Anybody Technologies A/S). His work integrates computational methods with experimental techniques, such as finite element analysis and synchrotron X-ray scattering. Key research themes include multi-scale mechanobiology, patient-specific implant design, and risk assessment via stochastic modelling. He has received awards including the NovaUCD Campus Company Development Programme (runner-up) and a biomedical engineering scholarship. Ongoing projects involve bioresorbable materials for orthopaedic and vascular applications, funded by initiatives like the European Training Network. Dr. Lennon's contributions span over 150 publications, with recent work focusing on bioresorbable vascular scaffolds, radiographic analysis in hip replacements, and nanocomposite material characterization. His lab develops technologies for medical device evaluation and regenerative medicine strategies, emphasizing translational research for clinical impact.

• Computer Vision
• Image Processing
• Machine Learning
• +13 more

Vasileios Argyriou is affiliated with Kingston University, specifically within the Faculty of Science, Engineering and Computing, and the School of Computer Science and Mathematics. He is an active researcher with a prolific publication record in computer vision, image processing, machine learning, and IoT applications. His research interests span a wide range of topics including Computer Vision , Photometric Stereo , Motion Estimation , Federated Learning , Remote Sensing , UAV-based Smart Farming , 3D Reconstruction , and Human-Computer Interaction . His work often bridges theoretical advances with real-world applications in agriculture, healthcare, and cybersecurity. The recent publications (2024–2025) highlight a strong focus on AI-driven solutions in agriculture (e.g., water stress detection), anomaly detection in video , secure federated learning , and advanced face reenactment using GANs . His methodologies frequently involve deep learning, sensor fusion, and simulation-based training. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While no explicit list of students or grants is provided, his extensive collaboration with researchers such as Panagiotis Sarigiannidis, Thomas Lagkas, Ilias Siniosoglou, and others suggests active supervision and participation in funded research projects, likely in EU or national programs related to IoT, AI, and smart systems. Labs and Teams: He appears to be part of a research group focused on intelligent systems, possibly involving the Intelligent Systems Research Group at Kingston University, with strong ties to Greek academic institutions. His work on UAVs, IoT, and federated learning indicates involvement in interdisciplinary teams developing smart, secure, and scalable AI solutions.

Daniel SieberView profile

Lecturer

• Medical Technology
• Health Technologies
• Surgical Simulation
• +6 more

Dr. Daniel Sieber serves as Head of the Department and Study Programs in Medical, Health, and Sports Technologies at MCI | The Entrepreneurial School®. He holds a Dr. rer. biol. hum. (Summa Cum Laude) from Hannover Medical School and a Dipl.-Ing. in Mechanical Engineering (Biomedical Engineering) from the University of Stuttgart. His academic roles include full-time lecturer and researcher with a clinical-medical focus. Research Interests: Dr. Sieber specializes in medical technologies, particularly focusing on surgical simulation using virtual reality (VR), cochlear implant innovation, minimally invasive surgery techniques, and 3D printing applications in healthcare. His work bridges engineering and medicine, emphasizing translational research and interdisciplinary education. Teaching & Leadership: He leads courses in Medical Technologies, Product Development, and Implantable Devices at MCI. Recent activities include visiting professorships in Shenzhen, China, and supervising over 20 bachelor's and master's theses in medical device engineering and surgical training technologies. Key Awards: 2023 MCI Teaching Award Patents: Includes designs for cochlear implant electrodes and surgical tools Networks: Member of Tyrol's Life Sciences Cluster Advisory Board Grants & Labs: His research has driven partnerships with institutions like Hannover Medical School and MED-EL GmbH. Active in developing VR surgical training platforms and digital twins for healthcare systems.