Find Academic Supervisors

• Medical Physics
• Radiation Dosimetry
• Computed Tomography
• +3 more

Maryam Bostani, Clinical Associate Professor in the Department of Radiological Sciences at UCLA's David Geffen School of Medicine, specializes in medical physics with a focus on radiation dosage optimization in computed tomography. Her work integrates Monte Carlo simulations and clinical validation to improve dose estimation methods. Research Interests: Dr. Bostani investigates radiation dosimetry, CT protocol optimization, and patient-specific dose modeling. Her research emphasizes: Development of size-specific dose estimation (SSDE) methodologies Monte Carlo simulations for organ dose prediction Tube current modulation techniques across diverse patient populations Radiation safety in obstetric and pediatric imaging Her publications demonstrate consistent focus on validating dose-reduction strategies in clinical CT applications, with recent work emphasizing fetal dose benchmarking and low-dose lung cancer screening protocols.

Erol VeznedarogluView profile

Professor

• Cerebrovascular Disorders
• Stroke Treatment
• Endovascular Neurosurgery
• +7 more

Erol Veznedaroglu, MD, is the Robert A. Groff Chair in Neurosurgery and Interim Chair of Neurology at Drexel University College of Medicine. He founded the Capital Institute for Neurosciences and leads the Global Neurosciences Institute (GNI), a consortium advancing comprehensive neurologic care across vascular, tumor, spine, and functional domains. His clinical expertise spans stroke, aneurysms, arteriovenous malformations, and neurocritical care. Education: MD - State University of New York at Buffalo School of Medicine (1996) BA - New York University (1989) Research Focus: Dr. Veznedaroglu pioneers endovascular therapies for cerebrovascular disorders. His work integrates stroke prevention, novel thrombectomy devices, and intra-arterial drug delivery for glioblastoma. Research emphasizes clinical translation, with trials like DAWN (thrombectomy in wake-up strokes) and FRED (intracranial aneurysm devices). Publication Trends: Recent articles reflect leadership in neurointerventional innovation, particularly thrombectomy techniques (Trevo/Solitaire devices), aneurysm management, and stroke systems optimization. Studies emphasize real-world efficacy, device safety, and protocol standardization. Awards & Honors: NJ Biz Power 50 Health Care (2014) Top Doctors, South Jersey Magazine (2010-2013) Academics in Medicine Award (2013) Guide to America's Top Surgeons (2007-2012) William A Buchheit Teaching Awards (2004-2008) Science Educator Award, Society for Neuroscience (2005) Grants & Leadership: Principal investigator for NIH-funded trials (e.g., POSITIVE, TREVO Registry) and industry studies (e.g., FRED, WEAVE). Directs GNI's multidisciplinary teams across 12+ subspecialties, including Alzheimer’s, Parkinson’s, and pediatric neurology. Infrastructure: Established Capital Health's neuro-emergency department reducing door-to-needle times for stroke thrombolysis. Developed simulation-based training curricula for endovascular techniques adopted by AANS/CNS.

Nima KasraieView profile

Associate Professor

• Medical Physics
• Radiation Safety
• Computed Tomography (CT)
• +4 more

Nima Kasraie, Ph.D., is an Associate Professor of Radiology at UT Southwestern Medical Center, specializing in Medical Physics. He holds dual board certifications in Nuclear Medicine Physics and Diagnostic Radiology Physics from the American Board of Science in Nuclear Medicine (ABSNM) and the American Board of Radiology (ABR). Dr. Kasraie is also licensed in Texas and serves as lead physicist at Children's Medical Center in Dallas and Children's Health in Plano. Education: Bachelor's in Physics: University of Tehran & Arkansas State University Master's in Nuclear and Radiological Engineering: University of Tennessee, Knoxville Ph.D. in Radiological Sciences: University of Texas Health Science Center San Antonio Postdoctoral Training: Biomedical Physics, California State University, Fresno Research Interests: Focuses on radiation safety in medical imaging, pediatric imaging protocols, and optimization of CT/PET-CT techniques. His work includes radiation dose reduction strategies, imaging phantom development, and clinical applications of advanced reconstruction algorithms (e.g., BSREM for Ga-PSMA PET). He has contributed to international initiatives like the CT dose benchmarking registry and pioneered phantom-based evaluation of pediatric airway imaging protocols. Dr. Kasraie also emphasizes improving communication of radiation risks to parents and training radiology residents in imaging physics. Affiliations: UT Southwestern Medical Center, Children's Medical Center Dallas, Children's Health Plano.

Russell C. HardieView profile

Professor

• Digital Signal Processing
• Image Processing
• Machine Learning
• +3 more

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

• Medical Physics
• Radiation Oncology
• Monte Carlo Simulation
• +4 more

Bruce Faddegon, PhD is a Professor in the Department of Radiation Oncology at the University of California, San Francisco (UCSF) School of Medicine. A board-certified clinical medical physicist, Dr. Faddegon is internationally recognized for his pioneering work in developing and applying Monte Carlo methods for treatment head simulation and dose calculation in radiotherapy. His research focuses on improving the accuracy and precision of radiation therapy through innovative computational approaches, with the overarching goal of enabling more aggressive cancer treatments while minimizing side effects. Dr. Faddegon earned his B.Sc. in Physics (honors) from the University of Victoria in 1977, followed by an M.Sc. in Physics (radiobiology) from the University of British Columbia in 1983, and completed his Ph.D. in Physics (radiotherapy) from Carleton University in 1990. His educational background provided the foundation for his subsequent contributions to medical physics and radiation oncology. Dr. Faddegon's research program centers on the development and application of Monte Carlo simulation techniques to advance radiation therapy. His primary focus is on the Tool for Particle Simulation (TOPAS), an open-source platform built on Geant4 that he co-developed. TOPAS has evolved from its initial application in proton therapy to encompass conventional radiotherapy, imaging, and radiobiology through extensions like TOPAS-nBio. His work addresses critical challenges including dose calculation accuracy, radiobiological modeling, and the development of cost-effective solutions to improve the therapeutic ratio in cancer treatment. Dr. Faddegon's research bridges physics, biology, and clinical applications, with particular emphasis on establishing the relationship between ionization deposition details, radiation chemistry, and biological effects. Analysis of Dr. Faddegon's most recent publications (2023-2025) reveals a strong focus on advancing Monte Carlo simulation capabilities for radiation therapy applications. His work spans from fundamental physics (track structure simulations, nanodosimetry) to clinical applications (treatment planning, secondary cancer risk assessment). A notable trend is the expansion of TOPAS capabilities into radiobiological modeling through TOPAS-nBio, which enables researchers to connect physical dose deposition with biological outcomes. His publications demonstrate increasing interdisciplinary collaboration, bridging physics, biology, and clinical oncology to address complex challenges in radiation therapy, with particular attention to improving precision in particle therapy and conventional radiotherapy. Dr. Faddegon has secured substantial research funding as Principal Investigator on multiple NIH grants, including the current U24 grant from the NCI Informatics Technology for Cancer Research program to extend TOPAS to conventional radiotherapy and imaging. His previous NIH funding includes R01 grants focused on proton therapy and radiation biology. These grants support his team's work in developing and validating Monte Carlo simulation tools that have become widely adopted in the medical physics community for applications ranging from treatment planning system validation to fundamental radiobiological research. Dr. Faddegon leads a research group focused on computational medical physics at UCSF that collaborates extensively with researchers at Massachusetts General Hospital, SLAC National Accelerator Laboratory, and other institutions worldwide. His team works on multiple aspects of the TOPAS simulation toolkit, including its application to clinical radiotherapy, radiobiological modeling, and medical imaging. Their work has established TOPAS as a standard tool in the field, with applications ranging from treatment planning system validation to fundamental radiobiological research, significantly advancing the precision and effectiveness of radiation therapy.

Yanping GongView profile

Assistant Professor

• Clinical medicine
• Clinical sciences
• Pathology
• +5 more

Yanping Gong is an Assistant Professor in the Department of Pathology and Molecular Medicine at Queen's University, affiliated with the School of Medicine within the Faculty of Health Sciences. She holds cross-appointments in the same department and is a member of The American Board of Pathology and The Royal College of Physicians and Surgeons. Her research focuses on clinical medicine, clinical sciences, pathology, and health sciences, with a strong emphasis on SARS-CoV-2 serology, antibody responses, and epidemiological studies in healthcare workers and vulnerable populations. Dr. Gong's work also includes developing diagnostic tools and advancing clinical laboratory standards. Education: She earned her MD (Doctor of Medicine) from Hebei Medical College. Her professional affiliations include Queen’s Laboratory for Molecular Pathology (QLMP), where she contributes to cutting-edge molecular diagnostic research. She is actively involved in consensus guidelines for SARS-CoV-2 serology testing and pediatric laboratory standards through initiatives like the CALIPER project. Research Trends: Her recent articles emphasize quantitative analysis of antibody responses to SARS-CoV-2 variants (e.g., Omicron), comparative evaluations of serological assays, and understanding immune dynamics in cancer patients and long-term care residents. She has pioneered bioluminescent biosensors for studying viral-receptor interactions and contributed to national pediatric critical value protocols. Grants & Advising: While specific grants are not detailed here, her cross-appointments and high-impact publications suggest active grant funding. She has no listed advisees at present. Labs/Teams: She collaborates with the Queen’s Laboratory for Molecular Pathology (QLMP) and interdisciplinary teams in virology, immunology, and clinical diagnostics.

• Healthcare Economics
• Cardiology
• Epidemiology
• +3 more

Miika Linna is a Visiting Professor in the Department of Industrial Engineering and Management at the University of Eastern Finland. With expertise in healthcare economics, cardiology, and epidemiology, his research focuses on optimizing healthcare systems and understanding disease management strategies, particularly in atrial fibrillation, multimorbidity, and stroke care. He has led two major projects funded by the Academy of Finland, aiming to improve social welfare and healthcare reform through data-driven solutions. His work addresses cost-effectiveness, treatment outcomes, and geographic disparities in healthcare access. Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (1999) Master's degree in Engineering and Technology, Helsinki University of Technology (1991) Research Interests: Healthcare cost analysis and efficiency Clinical epidemiology of cardiovascular diseases Policy implications of healthcare delivery models Population health monitoring Recent publications highlight trends in anticoagulant therapy, rural-urban healthcare disparities, and cost-effectiveness of mobile healthcare solutions. His work has been cited in over 50 peer-reviewed articles and featured in media coverage discussing atrial fibrillation management and pandemic vaccine impacts. Grants and Projects: Impro II (2021–2023): Enhanced data infrastructure for healthcare reform Impro (2018–2021): Optimizing service solutions in social welfare systems Labs/Teams: Collaborates with multidisciplinary teams in cardiology, epidemiology, and health policy research, leveraging national healthcare registries for large-scale analyses.

Peter L. ElkinView profile

Professor

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Natural Language Processing
• +3 more

Peter L. Elkin, MD, MACP, FACMI, FNYAM, is Professor and Chair of the Department of Biomedical Informatics and Professor of Internal Medicine at the University at Buffalo's Jacobs School of Medicine & Biomedical Sciences. He leads research in biomedical informatics, focusing on AI-driven healthcare solutions, terminology standards (e.g., SNOMED CT), NLP applications for clinical data, and analytics for substance use disorders. Affiliated with UBMD for patient care, he bridges clinical practice with translational research. Education: BS, Union College, Cum Laude (1978) MD, New York Medical College (1985) Residency, Lahey Clinic Fellowship in Biomedical Informatics, Massachusetts General Hospital (1990) Certificates: Biomedical Informatics (ACMI, 2006), ACP (2009), Internal Medicine (NYAM, 2012) His research integrates AI, NLP, and big data to address clinical challenges, including opioid/alcohol use disorders, long COVID phenotyping, and equitable ML algorithms. Recent work emphasizes semantic AI chatbots, generative AI infrastructure, and RNA editing implications in disease. Publications (2021–2025) reflect interdisciplinary trends: AI validation in medical exams (USMLE), opioid epidemiology in NY State, bias mitigation in ML, and NLP-enhanced EMR analytics. Themes include clinical AI ethics, public health informatics, and scalable terminology systems. Awards: Mastership, American College of Physicians (2009) Homer R. Warner Award (1998) Mayo Laureate Award (2005) Fellowships: ACMI (2006), NYAM (2012), Royal Danish Society (2007) He co-leads NIH/VA grants (e.g., CTSA, BD-STEP) and mentors through informatics training programs. As department chair, he oversees labs advancing NLP, clinical AI, and terminology standards.

• Pharmacology Education
• Cell Signaling
• Breast Cancer Research
• +3 more

Steven Tucker is a Professor of Pharmacology Education and Dean for Quality Assurance & Enhancement at the University of Aberdeen. He holds a SFHEA and FBPhS fellowships. Educated at the University of Aberdeen (BSc 1st Class 2000, PhD 2004), his career transitioned from research (postdocs at Aberdeen and Edinburgh) into academia with promotions from Teaching Fellow (2008) to Senior Lecturer (2012) before attaining his current professorship in 2021. Research focuses on breast cancer cell signaling (POPDC proteins) and pedagogic innovation, including core pharmacology curricula and interactive teaching tools. He leads 4 degree programs (BSc Pharmacology, MSc Clinical Pharmacology, etc.) and contributes to over 30 courses. Awards include the British Pharmacological Society's Rang Prize and multiple teaching excellence awards. He serves on 30+ internal committees (e.g., Quality Assurance Committee) and external roles like BPS Pharmacology Matters Editor-in-Chief. Collaborations include Swansea University's pharmacology practical models. Recent grants funded work on dose-response analysis tools (2023) and pharmacokinetics education resources (2014-2017).

Guillaume LandryView profile

Associate Professor

• Image Guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Deep Learning
• +12 more

Guillaume Landry is an Associate Professor of Image Guided Radiation Therapy at the Department of Radiation Oncology, University Hospital of Ludwig Maximilian University (LMU) in Munich, Germany, a position he has held since 2019. He is affiliated with the Faculty of Medicine and also contributes to teaching in the Faculty of Physics at LMU. He leads the IGRT research group, focusing on the integration of artificial intelligence in radiotherapy. His research interests include: Image Guided Radiation Therapy (IGRT) Artificial Intelligence and Deep Learning in Radiotherapy MR-guided Radiotherapy (MRgRT) Online Adaptive Radiotherapy Motion Management and Real-time Target Localization Auto-segmentation and Dose Accumulation CBCT and Proton Therapy Imaging His work emphasizes the application of deep learning to problems such as synthetic CT generation, motion prediction, and treatment adaptation, particularly in lung and abdominal cancers. Recent publications show a strong trend toward developing and validating AI models for clinical implementation in radiotherapy, including major contributions to guidelines and grand challenges like SynthRAD2025 and TrackRAD2025. Scientific awards received include: phiRO frequent reviewer award Emerging Talent Award ("Prix de la relève") Dr. Landry is actively involved in research mentoring and grant-funded projects such as the DFG-funded AIM-CBCT-ART and the BMBF-funded INSIGHT project. His work bridges medical physics, oncology, and AI, aiming to improve precision and personalization in radiation therapy. He is a key contributor to international collaborations and clinical guideline development in AI for radiation oncology. He is associated with the LMU ART Lab, a research group dedicated to advancing image-guided and adaptive radiotherapy through innovative physics and AI solutions.

Annette Kopp-SchneiderView profile

Professor

• Medical Biometry
• Biostatistics
• Clinical Trials
• +7 more

Prof. Dr. Annette Kopp-Schneider is Head of Department at the German Cancer Research Center (DKFZ) and affiliated with the Faculty of Medicine at the University of Heidelberg. She holds a professorial rank and leads research in medical biometry with a focus on clinical trial methodology in precision oncology. Diploma in Mathematics, RWTH Aachen, 1984 Dr. rer. nat. in Mathematics, RWTH Aachen, 1987 Habilitation in Medical Biometry, University of Heidelberg, 1997 Her research interests include clinical trials for precision oncology , dose-response modeling , and biologically based mechanistic modeling of cell systems . She develops advanced statistical methods for trial design, particularly in pediatric oncology and settings with small sample sizes. Her work emphasizes rigorous control of type I error when borrowing historical data and optimal experimental designs for drug combinations. The recent publications reflect a strong trend in methodological biostatistics , particularly in Bayesian and frequentist approaches to clinical trial design , external data incorporation , and transparent evaluation in biomedical image analysis . Her contributions span oncology, toxicology, and translational medicine, with a consistent focus on statistical rigor and reproducibility. She has contributed to major initiatives such as the Pediatric Precision Oncology INFORM Registry, advancing personalized treatment for children with cancer. Her methodological work supports fair comparisons in hybrid and single-arm trials and improves the reliability of challenge-based research in medical imaging. Prof. Kopp-Schneider is actively engaged in grant-funded research and mentoring within her department. She leads a team focused on biostatistical innovation in clinical research, particularly in oncology settings where data are sparse and traditional designs face limitations. She is involved in collaborative teams working on biomedical image analysis challenges and pediatric cancer registries. Her lab contributes to open science through toolkits for challenge analysis and transparent reporting guidelines like BIAS (Transparent Reporting of Biomedical Image Analysis Challenges).

Kathryn MorbitzerView profile

Teaching Professor

• Pharmacy Education
• Health System Pharmacy Administration
• Neurocritical Care Pharmacotherapy
• +3 more

Kathryn Morbitzer, Pharm.D., M.S., is a Teaching Professor at the UNC Eshelman School of Pharmacy and serves as Curriculum Design & Innovation Director. She holds multiple leadership roles, including Associate Director of the Center for Innovative Pharmacy Education & Research (CIPhER), Assistant Director of Pharmacy Administration and Leadership, and Assistant Director of the MS in Pharmaceutical Sciences program. Her academic background includes a Pharm.D. from Wayne State University and an M.S. in Biomedical and Health Informatics from UNC-Chapel Hill, complemented by PGY1 residency training and an academic fellowship in neurocritical care pharmacotherapy. Education: Pharm.D. (Wayne State University, 2012), M.S. Biomedical Informatics (UNC-Chapel Hill, 2017) Residencies: PGY1 Pharmacy Practice (Medical University of South Carolina, 2012–2013) Fellowships: Neurocritical Care Pharmacotherapy (UNC Eshelman School of Pharmacy, 2013–2015) Her research focuses on expanding pharmacy student opportunities in non-direct patient care roles, optimizing teaching practices, and improving outcomes in neurocritical care. Key areas of interest include health-system pharmacy administration , clinical pharmacotherapy in acute care , and innovations in pharmacy education . Recent work explores the impact of AI tools like ChatGPT on student evaluations and the design of competency-based training programs. Publications emphasize neurocritical care pharmacokinetics (e.g., vancomycin dosing in brain injury patients) and educational initiatives like microcredentials and residency training. Morbitzer has contributed to guideline development for glycemic control and renal function monitoring in critically ill patients. Her professional development efforts include formative coaching programs for pharmacy educators and initiatives to diversify career pathways for underrepresented groups in pharmacy. She actively participates in interprofessional education through grand rounds presentations and continuing education programs.

Harvey FriedmanView profile

Professor

• Genital herpes vaccine development
• HSV immune evasion mechanisms
• Virology
• +6 more

Harvey Friedman is a Professor of Medicine (Infectious Diseases) at the Perelman School of Medicine, University of Pennsylvania . His research focuses on developing vaccines for genital herpes caused by HSV-1 and HSV-2, with particular emphasis on understanding and counteracting viral immune evasion strategies through glycoproteins gC and gE. Education: B.S. and M.D. from McGill University (1965, 1969) Research Interests include: Vaccine design using nucleoside-modified mRNA in lipid nanoparticles Targeting HSV glycoproteins gC, gD, gE to block viral entry and immune evasion Preclinical models ( mice, guinea pigs, rhesus macaques ) for vaccine testing Therapeutic applications for reducing outbreak frequency in infected individuals Complement system interactions with HSV pathogens Selected Publications demonstrate: Trivalent mRNA vaccines achieving 50-100x reduced virulence in animal models Key insights into HSV immune evasion mechanisms via gC/gE Transition to human clinical trials by 2022 for prevention Expanding vaccine applications to oral herpes and neonatal protection Lab Personnel include: Dr. Sita Awasthi (Research Associate Professor) Dr. Lauren M. Hook (Research Assistant Professor) Dr. Kevin Egan (Post-doctoral Fellow) Dr. Angela Desmond (Instructor in Pediatric Infectious Disease) Dr. John Lubinski (Senior Research Scientist) Bernard Fowler (Research Specialist) Zauraiz Syeda (Research Specialist) Valerie Bromberg (Undergraduate researcher) Current projects evaluate mRNA vaccine efficacy against neonatal herpes, therapeutic applications for outbreak reduction, and cross-protection against oral herpes infections.

Carola-Bibiane SchönliebView profile

Professor

• Image Analysis
• Image Processing
• Inverse Problems
• +3 more

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.

• Radiobiology
• Computational Biology
• Medical Data Analysis
• +3 more

John O'Connor is a Lecturer in Engineering Data Analysis at the School of Engineering, Ulster University. His work bridges biomedical engineering, radiation oncology, and computational biology, with a focus on improving radiotherapy outcomes through advanced data modeling. Key affiliations: School of Engineering, Faculty of Computing, Engineering and the Built Environment Current projects: BANFA (Royal Society-funded) on neurovascular function assessment Research interests span radiobiology , radiosensitivity modeling , and neurocardiovascular data analysis . He develops deep learning tools for radiotherapy planning and investigates biomechanical factors in aging-related brain health . Recent articles highlight trends in: Optimizing proton therapy and radiation dosimetry for cancer treatment Analyzing transcriptomic signatures of radiosensitivity Understanding orthostatic hemodynamic responses in aging populations He leads the Royal Society-funded project BANFA , which examines biomechanically adjusted neurovascular function in relation to blood pressure dynamics and brain aging.