Find Academic Supervisors

• Medical Imaging
• Nuclear Engineering
• Biomedical Engineering
• +12 more

Spencer L. Bowen, Ph.D., is an Assistant Professor in the Department of Radiology at UT Southwestern Medical Center, where he is a member of the Radiology Research section and serves as a PET research scientist. His work is centered on advancing nuclear imaging technologies for clinical and research applications in oncology, neurology, and cardiology. Education: Bachelor's in Biomedical Engineering – University of Washington, Seattle Ph.D. in Biomedical Engineering – University of California, Davis Research Fellow – Massachusetts General Hospital, Charlestown, MA Dr. Bowen's research focuses on the development of advanced PET imaging systems, including dedicated breast PET/CT scanners and hybrid PET-MR technologies. He investigates image acquisition techniques, reconstruction algorithms, attenuation and scatter correction methods, and partial volume correction to improve quantitative accuracy. His work spans hardware design, software development (e.g., the Masamune processing tool), and clinical translation. His recent publications highlight innovations in cardiac and neurological PET quantification, breast imaging, and hybrid PET/MR systems. Themes include attenuation correction in PET/MR, dynamic PET modeling, and the impact of image processing on clinical interpretation. Scientific Recognition: Research featured on the cover of the Journal of Nuclear Medicine Work covered by press outlets Dr. Bowen actively contributes to the scientific community as a reviewer for leading journals including Journal of Nuclear Medicine , Medical Physics , Physics in Medicine and Biology , and IEEE Transactions on Nuclear Science and Transactions on Medical Imaging . His lab, the Bowen Lab, is engaged in ongoing research and is currently recruiting PhD graduate students, indicating active grant support and research momentum. He leads a research team focused on developing tomographic tools for precision medicine. The Bowen Lab is dedicated to creating and refining nuclear imaging technologies to enhance both clinical care and scientific discovery, with a strong emphasis on quantitative, high-resolution imaging across multiple disease domains.

• Positron Emission Tomography (PET) Imaging
• Neurochemistry
• Computational Neuroscience
• +4 more

Professor Evan Morris is a faculty member at Yale School of Medicine in the Radiology and Biomedical Imaging department, with secondary appointments in Biomedical Engineering and Psychiatry. His work combines advanced kinetic modeling and dynamic PET imaging to visualize neurotransmitter dynamics in the brain. PhD in Chemical Engineering from Case Western Reserve University (1991) Postdoctoral fellowship at Massachusetts General Hospital (1995) Morris specializes in creating "dopamine movies" through PET imaging, revealing transient neurochemical fluctuations related to addiction, Parkinson's disease, and stress responses. His group develops novel parametric imaging methods for faster drug discovery and disease biomarker identification. Recent publications focus on: Nonsteady-state PET modeling Stress-induced opioid receptor connectivity Exercise effects on Parkinson's neuropathology EC50 image quantification Scientific recognitions include: Fulbright Senior Scholar (2015) Yale Graduate Mentor of the Year (2013) He serves as Principal Investigator for studies on nicotine addiction and collaborates across Yale's Morris Lab , PET Core , and Neural Disorders programs, advancing applications in Medical Imaging , Neurochemistry , and Biomedical Engineering .

• Positron Annihilation
• Materials Science
• Condensed Matter Physics
• +4 more

Paulo Manuel Antunes Mendes Gordo is an Assistant Professor at the University of Coimbra’s Faculty of Science and Technology since 2005. He holds a PhD in Experimental Physics (2005, University of Coimbra) and a Bachelor’s degree in Physics (1988, University of Coimbra). His research focuses on defects in materials, utilizing positron annihilation techniques, and spans fields like condensed matter physics, polymer science, and nuclear medicine. Key research interests include characterizing defects in semiconductors, polymers, and ceramics using advanced spectroscopic methods. He has explored topics such as hydrogen interactions in zirconia, drug delivery systems via polyurethane nanotechnology, and radiation detection instrumentation for medical imaging. His work bridges computational modeling and experimental validation, addressing challenges in material stability and structure-property relationships. Publications trends reflect a strong emphasis on materials science, with contributions to journals like Physical Review B , Journal of Pharmaceutical Sciences , and Physica Scripta . Research often intersects interdisciplinary applications, such as medical imaging devices and polymer-based drug carriers. No scientific awards are explicitly mentioned in the provided information. His career includes prior roles as a junior researcher at the University of Aveiro (1990–1992) and early-career positions at the University of Coimbra (1993–2005). No grants or lab affiliations were detailed in the text, though collaborations with institutions like the University of Aveiro and international teams are evident from co-authorship patterns.

Eike SchneidersView profile

Lecturer

• Human-Computer and Robot Interaction
• Human-AI Interaction focusing on generative AI and its impact on society
• Multidisciplinary research in collaboration with artists, medical professionals, and lawyers

Dr. Eike Schneiders is a Lecturer at the School of Electronics and Computer Science, University of Southampton. He holds a PhD in Computer Science from Aalborg University and is a recipient of the UKRI Trustworthy Autonomous Systems Hub Early Career Researcher Award. His research focuses on autonomous and AI-infused systems, investigating how to ensure their trustworthiness and responsible interaction with humans and other agents through interdisciplinary collaborations across art, law, and medicine. His recent work explores the societal impact of Generative AI, particularly examining its influence on legal services accessibility for non-experts through the REGALS project (Principal Investigator) and its effects on creative communities via the ART-I project (Co-Investigator). Dr. Schneiders emphasizes public engagement to enhance AI literacy and employs both qualitative and quantitative methods to study human-computer interaction and ethical implications. Dr. Schneiders teaches courses including COMP6251 (Web and Cloud Application Development), ELEC6259 (Research Methods and Project Preparation), and ELEC6200 (Group Design Project). His pedagogical approach integrates foundational technical skills with critical research methodologies. He has led several interdisciplinary projects including ARTificial Intelligence: Show Me a Story about AI (ART-I) and Responsible Employment of Generative AI for Legal Services (REGALS), and contributes to the Agents, Interaction and Complexity research group. His Cat Royale project received the 2024 Webby Award for Best Integrated Experience. Honors include the Best Paper Award at ACM CHI 2024, multiple Honourable Mentions at CHI and TAS Symposiums, and membership in the Foundation for Science and Technology's Future Leaders Programme.

Peter VerwilstView profile

Associate Professor

• Medicinal Chemistry
• Allosteric Modulation of Enzyme Targets
• Fluorescent Markers for Neurodegenerative Diseases
• +5 more

Peter Verwilst is an Associate Professor at the Medicinal Chemistry division of the Rega Institute for Medical Research , part of KU Leuven 's Department of Pharmaceutical and Pharmacological Sciences. He leads projects in allosteric modulation of enzyme targets , fluorescent markers for neurodegenerative diseases , and novel antimicrobial development , particularly focusing on Gram-negative bacteria and HIV-related therapies. His research integrates computational modeling , chemical synthesis , and fluorescent probe design . He supervises PhD students including Margaux Billen , Eline Goffin , and Radu Bulai , and collaborates with institutions like Masaryk University and the Institut Pasteur de Lille. Current projects explore CCR5 signaling modulators , PurK inhibitors , and DNA-Encoded Libraries for P. aeruginosa antibiotics. Scientific awards include supporting students like Radu Bulai in obtaining FWO PhD Fellowships . His teaching includes Organische chemie I & II and Medicinale chemie courses. The lab recently celebrated securing a C1 grant and welcomes international collaborations.

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

Roger Fulton is a Professor of Medical and Preclinical Imaging at the University of Sydney and a Principal Nuclear Medicine Physics Specialist at Westmead Hospital's Department of Medical Physics. He is part of the Brain and Mind Centre and leads the Physics and Biomodelling team. His roles include Conjoint Professor in Medical Radiation Sciences, RHD student supervision, and coordination of the MRTY5134 (Computed Tomography Theory) and MRTY2107 (Imaging Technology 2) units of study. He holds a PhD in Applied Physics. His career has spanned collaborations with institutions like the Research Center Julich (Germany), Massachusetts General Hospital (USA), and the International Atomic Energy Agency (Austria). Professional recognitions include Fellow of ACPSEM, Senior IEEE Membership, and Chair of the Nuclear Medical Imaging Sciences Council (IEEE). Research interests focus on motion tracking/correction for SPECT, PET, and CT imaging, including patent innovations and leadership in radiation dose reduction techniques. He has secured $9.2M in competitive grants since 2004 and contributed to global standards through IAEA initiatives. Grants and advising: Over $9.2M in grants since 2004, advising PhD student Bader ALOUFI on liver imaging kinetics. His work bridges hardware engineering (e.g., Intel RealSense depth cameras) with software solutions (neural networks, Bayesian methods) to enhance imaging accuracy and reduce patient motion artifacts. Scientific Awards: US Patent (2020) ARC College of Experts (2016–2018) ACPSEM Fellowship IEEE Senior Membership IEEE Nuclear and Plasma Sciences Society leadership Labs/Teams: Physics and Biomodelling team at the Brain and Mind Centre, collaborating with international groups on preclinical imaging systems and awake-animal PET methodologies.

• Nuclear Medicine
• Medical Imaging
• Biomedical Engineering
• +4 more

Dale Bailey is a Professor in the Faculty of Medicine & Health at the University of Sydney, with clinical appointments at Royal North Shore Hospital's Department of Nuclear Medicine. He previously directed the Sydney Vital Northern Translational Cancer Research Centre and is a member of the Centre for Drug Discovery Innovation, the University of Sydney Nano Institute, and the Charles Perkins Centre. Nuclear Medicine Physicist Theranostics Researcher Clinical Imaging Expert His research focuses on quantitative functional imaging using SPECT and PET, hybrid imaging modalities, radiation dosimetry for personalized cancer therapy, and the biological effects of radiation. He pioneered combined structure/function imaging and advanced radionuclide therapy applications. Recent publications emphasize PET/CT with novel radionuclides, metabolic tumor volume as a prognostic biomarker, and dosimetry validation methods . Key themes include cancer, nuclear medicine, and translational research. PhD in Physics Fellow of the Institute of Physics and Engineering in Medicine (FIPEM) Chartered Scientist (CSci) in the UK International Consensus Guidelines Contributor He supervises research students in radiotheranostics and collaborates on clinical trials for neuroendocrine tumors and glioblastoma. Grants and projects involve hybrid imaging optimization, radiation safety, and personalized treatment planning.

Gregory S. OkinView profile

Professor

• Dryland Geomorphology
• Aeolian Processes
• Mineral Aerosols
• +4 more

Gregory S. Okin is a Professor of Geography and Chair of the Department of Geography at UCLA, affiliated with the Institute of the Environment and Sustainability. He holds a PhD in Geochemistry from Caltech (2001) and has been at UCLA since 2006. His research focuses on dryland geomorphology, aeolian processes, and mineral aerosol dynamics, with emphasis on dust emission impacts on climate, ecosystems, and human health. Okin employs remote sensing, field studies, and modeling to analyze soil-vegetation-atmosphere interactions in arid regions, particularly wind erosion's role in grassland-to-shrubland transitions. He co-leads NASA's Earth Surface Mineral Dust Source Investigation (EMIT), creating global mineral maps using imaging spectroscopy. Recent work addresses dust-climate links, wildfire health impacts, and rangeland monitoring via satellite-cloud computing integration. Education Background: Ph.D., Geochemistry, California Institute of Technology (2001) M.S., Geology, California Institute of Technology (1997) B.A., Chemistry & Philosophy (Double Major), Middlebury College (1995) Postdoctoral Research, Geography, UC Santa Barbara (2001–2002) Research Interests: Dr. Okin's work integrates aeolian geomorphology , remote sensing , and Earth system modeling to explore: - Dust emission mechanisms and climate feedbacks - Dryland ecosystem resilience under climate change - Vegetation-soil connectivity in arid landscapes - Applications of imaging spectroscopy (e.g., EMIT mission) His projects often involve interdisciplinary collaborations, combining field measurements with computational modeling. Scientific Contributions: Key innovations include developing dust emission models (e.g., MAPTALE) and advancing spectral unmixing techniques for mineral mapping. His findings on pet food environmental impacts (e.g., carbon pawprints) have gained media attention.

David S. WackView profile

Associate Professor

• Artificial Intelligence
• Biomedical Engineering
• Brain Research
• +5 more

David S. Wack is an Associate Professor at the University at Buffalo's Jacobs School of Medicine & Biomedical Sciences, Department of Radiology. His research focuses on medical image analysis and neuroimaging, particularly in auditory processing and parameter estimation from PET, MRI, and CT images. He has held academic positions since 1992, progressing from instructor roles to his current rank. His work includes NIH-funded projects on imaging technologies and collaborations with institutions like Canon Medical Systems and UBNS. Education: PhD in Communicative Disorders and Sciences (SUNY Buffalo, 2010) MA in Applied Mathematics (SUNY Buffalo, 1992) BA in Applied Mathematics and Music Performance (SUNY Buffalo, 1989) Research Interests: Development of parametric maps from medical images Machine learning applications in neuroimaging Neuroimaging of auditory and language processing Dynamic image noise reduction and segmentation algorithms Neural markers of top-down compensation in speech processing Recent Projects: NIH-funded self-collimating SPECT breast tomography system 4D flow MRI for intracranial atherosclerotic disease assessment Machine learning for neuroimage classification Grants & Service: Member of Jacobs School faculty council and UB Faculty Student Association board Editorial roles at Frontiers in Neuroscience (Brain Imaging Methods, Decision Neuroscience) Labs & Teams: Buffalo Neuroimaging Analysis Center Collaborations with UBNS, Canon Medical, and VA Western New York Health Care System

Stephen PistoriusView profile

Professor

• Medical Physics
• Medical Imaging
• Image Reconstruction
• +4 more

Stephen Pistorius is a Professor in the Department of Physics and Astronomy at the University of Manitoba's Faculty of Science. He serves as Associate Head and Director of the Medical Physics Program, focusing on interdisciplinary research at the intersection of medical physics, biomedical engineering, and artificial intelligence. Role : Professor, Associate Head, Director of Medical Physics Program Contact : Office 205 Allen Building, Stephen.Pistorius@umanitoba.ca , +1 204-474-6205 Research Interests Medical Imaging : Development and optimization of radar-based microwave sensing systems for breast cancer detection. Image Reconstruction : Innovation in computational algorithms for PET and microwave imaging. Artificial Intelligence : Application of machine learning to tumor detection and signal analysis. Publication Trends His recent work emphasizes microwave imaging hardware (antenna arrays, phantom materials), machine learning integration for tumor detection, and image quality metrics across modalities. Key subfields include radar systems , stochastic optimization , and clinical translation of microwave sensing . Additional Contributions He contributes to radiation oncology via EPID-based positioning automation and dose verification systems, while also addressing technical challenges in 3D-printed phantom modeling and microwave propagation in biological tissues.

• Privacy
• Security
• Algorithmic Accountability
• +5 more

Bogdan Kulynych is a research scientist at Lausanne University Hospital in Switzerland, working within the Clinical Data Science group. He holds a Ph.D. in Computer Science from EPFL (Switzerland), where he was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy in Ukraine. His academic journey also includes a visiting fellowship at Harvard University with Flavio du Pin Calmon, and internships at Google and CERN. His research spans three interconnected domains: Algorithmic Accountability, Verification, and Reliability; Privacy-Preserving Learning and Statistics; and Algorithmic Systems in Healthcare. Kulynych develops methods for obtaining practical guarantees on model stability, robustness, and reliability, while also auditing these properties. His privacy work focuses on systems ensuring practical privacy guarantees with legally legible and interpretable operational risk analyses. In healthcare, he critically studies algorithmic system deployment in clinical practice through collaboration with clinicians and medical informatics practitioners. Kulynych's publication record demonstrates significant impact in top venues including NeurIPS, ICML, ICLR, FAccT, and PETS. His recent work addresses fundamental questions in differential privacy, operational privacy metrics, and healthcare AI applications. His research trend shows increasing focus on translating theoretical privacy guarantees into practical healthcare settings while addressing the social implications of algorithmic systems. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy (NeurIPS 2025) (ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees (2025) Attack-Aware Noise Calibration for Differential Privacy (NeurIPS 2024) As an active member of the academic community, Kulynych regularly presents at major conferences and seminars, including recent talks at Harvard Privacy Tools Seminar, NeurIPS, and Imperial College London. His work has received media attention in The Guardian, Wired, The Verge, and CNET regarding algorithmic bias challenges. Kulynych maintains an active presence on Bluesky (@bogdankulynych) where he engages in critical discussions about AI ethics, privacy, and the societal implications of technology.

• Medical Imaging
• Positron Emission Tomography (PET)
• Detector Physics
• +12 more

David Schug is a Group Leader in PET detector research at the Chair of Imaging and Image Processing, RWTH Aachen University, Germany. He is also Co-Founder and Co-Director of Hyperion Hybrid Imaging Systems GmbH and serves as a Radiation Safety Officer. His research is centered on advancing positron emission tomography (PET) technology, particularly in detector design, time-of-flight performance, depth-of-interaction encoding, and integration with MRI systems. He is actively involved in both academic and industrial innovation in medical imaging. B.Sc. Physics, RWTH Aachen University (2006–2009) M.Sc. Physics, RWTH Aachen University (2009–2011) Dr.rer.nat., RWTH Aachen University (2012–2015) David Schug's research focuses on medical imaging physics , particularly PET detector development , hybrid PET/MRI systems , and advanced calibration techniques . His work integrates machine learning with physics-based models to enhance timing resolution and image quality. Key areas include ASIC-based readout electronics, RF shielding for MRI compatibility, and real-time data processing. He has made significant contributions to improving coincidence time resolution beyond 200 ps in clinical and preclinical systems. His recent publications demonstrate a strong trend in high-resolution PET detector design , with emphasis on time-of-flight capabilities , depth-of-interaction encoding , and in-system calibration methods . Articles frequently involve machine learning (e.g., gradient tree boosting), ASIC integration (TOFPET), and MRI compatibility. The research spans both preclinical and clinical applications, including breast cancer imaging, brain PET, and proton therapy monitoring. David Schug has not been publicly awarded any scientific prizes or fellowships based on available information. He advises several researchers and students involved in PET detector projects, as evidenced by co-authorship on numerous publications. His team develops cutting-edge detector platforms like the Hyperion series and collaborates on EU projects such as HYPMED. While specific grant details are not listed, his sustained research output and entrepreneurial activity suggest successful funding acquisition. His work bridges academia and industry through Hyperion GmbH. David Schug leads the PET detector research group at the Chair of Imaging and Image Processing, RWTH Aachen. He is part of AG START at Uniklinik RWTH Aachen and collaborates extensively with Prof. Volkmar Schulz. His team focuses on the Hyperion detector platform, developing inserts for simultaneous PET/MRI, with applications in oncology, neurology, and cardiology.

• Medical Imaging
• Neuroscience
• Radiology
• +3 more

Dr. Christoph Lerche is a researcher at the Institute of Neuroscience and Medicine (INM) , specifically in the Physics of Medical Imaging (INM-4) department at Forschungszentrum Jülich GmbH . His research focuses on advanced medical imaging technologies, particularly positron emission tomography (PET) and hybrid imaging systems like PET-MRI. Key areas include improving PET detector design, developing novel reconstruction algorithms, and applying these techniques to neurodegenerative diseases (e.g., Alzheimer’s) and oncology (e.g., glioma diagnosis). His work emphasizes interdisciplinary collaborations, such as optimizing BrainPET insert compatibility with ultra-high field (UHF) MRI systems and analyzing sleep-related effects on synaptic density. He contributes to clinical applications, such as distinguishing glioma relapse from treatment effects using FET PET radiomics, and advancing multimodal imaging (e.g., simultaneous PET/MR/EEG). Notable projects include enhancing dead-time correction for accurate BrainPET quantification, designing dipole antenna arrays for hybrid systems, and developing photon-counting CT-based PET attenuation maps. His innovations aim to bridge gaps between imaging hardware, data processing, and clinical neuroscience.

Timothy Garvey TurkingtonView profile

Associate Professor

• PET Imaging Physics
• Instrumentation
• Image Reconstruction
• +5 more

Timothy Garvey Turkington is an Associate Professor in the Department of Radiology at Duke University School of Medicine and a Faculty Network Member of the Duke Institute for Brain Sciences. Holding a Ph.D. from Duke University (1989), his academic career spans over three decades with continuous contributions to nuclear medicine physics. His research focuses on PET imaging physics, including instrumentation development, reconstruction algorithms, and image processing techniques. Key research thrusts include improving quantitative accuracy in PET, reducing scan times and radiation doses in PET/CT systems, developing novel imaging devices for PET and SPECT applications, and expanding PET's clinical utility in oncology, neurology, and cardiology. His work bridges theoretical physics with practical clinical implementation, particularly in breast imaging and quantitative biomarker development. Analysis of his 15 most recent publications reveals a strong emphasis on instrumentation optimization (particularly for breast imaging), quantification accuracy in clinical settings, and the development of standardized protocols for PET/CT systems. His work increasingly focuses on translational applications where physics innovations directly impact clinical decision-making, especially in cancer imaging and therapy response assessment. Dr. Turkington teaches graduate courses including PHYSICS 523: Modern Medical Diagnostic Imaging System, MEDPHY 782: Advanced Practicum for Clinical Development in Medical Physics, and MEDPHY 530: Modern Medical Diagnostic Imaging System, training the next generation of medical physicists. His research has been supported by significant grants including the RSNA 2016 Project (2014-2018), Simultaneous Emission and Transmission Mammotomography (2002-2014), and the Harmonized PET Reconstructions for Cancer Clinical Trails (2013-2014), demonstrating sustained funding for his innovative work in medical imaging physics.

Xin XingView profile

Assistant Professor

• Medical Imaging
• Machine Learning
• Neuroscience
• +3 more

Xin Xing is an Assistant Professor at the University of Nebraska at Omaha, College of Information Science & Technology, Department of Computer Science. Their research bridges machine learning, neuroscience, and biomedical applications. Education: Ph.D. in Computer Science (2023), University of Kentucky M.S. in Information Technology (2016), University of Stuttgart B.S. in Communications Engineering (2011), Shandong University Research Interests: Medical Imaging AI: Developing models for Alzheimer's disease diagnosis using 3D PET/MRI and transformers (e.g., ADViT, CAT-XPLAIN) Gut-Brain Axis: Investigating microbiome impacts on neurodegeneration, particularly in APOE4 carriers Computer Vision: Innovating diffusion models, self-supervised learning, and attention mechanisms for biomedical and geospatial applications Publication Trends: Recent work focuses on neuroimaging biomarkers for Alzheimer's, microbiome interventions, and scalable vision-language architectures. They integrate cutting-edge ML techniques with clinical data analysis. Collaborations: Engaged in interdisciplinary projects spanning genetics, nutrition, and traffic safety. Their work appears in journals like Communications Biology and explores real-world applications such as roadway hazard detection via satellite imagery.