جستجوی استاد راهنمای دانشگاهی

• Iron Homeostasis
• Cell Physiology
• Systems Biology
• +۴ مورد دیگر

Dr. Samira Lakhal-Littleton is an Associate Professor of Cell Physiology and MRC Senior Non-Clinical Research Fellow at the University of Oxford, affiliated with the Department of Physiology, Anatomy and Genetics and Brasenose College. Her research focuses on iron homeostasis, systems biology, and oxygen sensing mechanisms in cardiovascular and systemic physiology. Education: BSc in Human Genetics (University College London), DPhil in Molecular Medicine (University of Oxford) Her work bridges cell physiology and translational medicine, with key discoveries on hypoxia-inducible factors (HIFs), iron regulatory genes TMPRSS6 and GDF15 , and the role of hepcidin in altitude adaptation and chronic diseases. She utilizes tissue-specific animal models to study iron regulation in heart, kidney, placenta, and vasculature. Recent publications highlight her contributions to understanding: FLASH radiotherapy effects linked to iron-dependent lipid peroxidation Clinical implications of myocardial iron dynamics in heart failure Hepcidin's role in vascular protection and placental iron transfer Biomarker development for predictive iron deficiency diagnostics Scientific Leadership: British Heart Foundation Intermediate Fellowship (2012) MRC Senior Fellowship (2020) BioIron Society Board Member (2019) She collaborates with clinical teams on translational projects and serves as a Tutorial Fellow in Medicine at Brasenose College, mentoring students in physiological sciences.

• Additive Manufacturing
• Aerospace Engineering
• Thermofluids
• +۱ مورد دیگر

Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems

• Medical Imaging
• Computed Tomography
• Inverse Problems
• +۳ مورد دیگر

Dr. Owen Dillon is a Research Fellow in the Discipline of Medical Imaging Sciences at the University of Sydney's Faculty of Medicine and Health. He holds affiliations with the ACRF Image X Institute and the Dodd-Walls Centre for Photonic and Quantum Technologies. His work focuses on advanced imaging techniques for medical applications, particularly computed tomography (CT) and motion compensation in radiation therapy. He completed his PhD in Mathematics at the University of Auckland, specializing in probabilistic compression algorithms for inverse problems. Education: B.Sc. Physics & Applied Mathematics (2013, University of Auckland), First Class Honours in Mathematics (2015), PhD Mathematics (2018). Research interests include inverse problems, Bayesian statistics, CT image reconstruction, and real-time imaging systems. Current projects involve optimizing CT acquisition geometries, motion-compensated 4D imaging, and anatomical motion estimation. His contributions have led to clinical trials reducing radiation dose and scan times. He advises two PhD students and collaborates on grants like the Quantum CT project. Grants: 'Quantum CT for Cancer Diagnosis' (2024), 'Functional Imaging in Lung Cancer' (2024). His work bridges mathematical theory with clinical applications in oncology and interventional radiology.

• Radiation Oncology
• Medical Physics
• Treatment Planning Techniques
• +۵ مورد دیگر

Dr. Sotirios Stathakis is Chief of Physics at the Mary Bird Perkins Cancer Center (2023–Present) and Associate Director of the Medical Physics Division at the University of Texas Health Science Center San Antonio (2017–2022). He holds an Adjunct Professor position at Louisiana State University's Department of Physics and Astronomy (2023–Present). His expertise lies in radiation oncology and medical physics, with a focus on patient-specific quality assurance, dose verification, and advanced treatment techniques like adaptive radiation therapy and SBRT. Education: Ph.D., Medical Physics, University of Patras (2005) M.S., Medical Physics, University of Aberdeen (1997) B.S., Physics (minor in Mathematics and Computer Science), University of Waterloo (1995) Research Interests: Patient-specific quality assurance Daily dose verification Treatment planning techniques Adaptive radiation therapy Stereotactic body radiation therapy (SBRT) Automation and workflow optimization in radiation oncology Publications: Over 20 peer-reviewed articles since 2020, focusing on topics like Monte Carlo simulations, AI-driven beam analysis, and AAPM task group recommendations for IMRT verification. Key themes include improving dose accuracy, equipment validation, and clinical implementation of advanced radiation technologies. Grants & Awards: Not explicitly listed in provided text. Labs/Teams: Collaborates with institutions like Fox Chase Cancer Center, South Texas Veterans Health Administration, and LSU on medical physics research and clinical applications.

Ananya Choudhuryمشاهده پروفایل

استاد

• Radiation Therapy
• Prostate Cancer
• Bladder Cancer
• +۹ مورد دیگر

Professor Ananya Choudhury serves as Chair and Honorary Consultant in Clinical Oncology at the University of Manchester, where she is also Co-Group Leader of the Translational Radiobiology Group within the Division of Cancer Sciences. She joined The Christie NHS Foundation Trust in 2008, specializing in urology and sarcoma, and has since focused on radiotherapy-related research in prostate and bladder cancers. Professor Choudhury is clinical lead for advanced radiotherapy, including the groundbreaking MRLinac project, and plays a key role in national radiotherapy research initiatives. Professor Choudhury earned her BA (Hons) in 1993, MB. BChir (Cantab) in 1995, and MA (Cantab) in 1997 from Trinity College, Cambridge. She completed her Clinical Oncology training at the Yorkshire Deanery from 2000-2008, during which she earned her MRCP in 2000 and F.R.C.R in 2004. She completed her PhD in 2008 through the University of Leeds and Princess Margaret Hospital in Toronto, Canada, where she studied the molecular epidemiology of DNA double strand break repair in bladder cancer. Professor Choudhury's research program focuses on optimizing and personalizing radiotherapy using advanced imaging technology to deliver high doses while minimizing side effects. Her work centers on prostate and bladder cancers, with particular interest in predictive biomarkers, hypoxia, and the integration of magnetic resonance imaging to improve treatment precision. She has pioneered research in radiotherapy dose optimization, biomarker development, and the identification of patients who would benefit most from different treatment approaches. Her extensive publication record demonstrates a strong focus on radiation therapy, particularly in genitourinary cancers. Recent work explores MRI-guided radiotherapy, hypoxia biomarkers, and personalized treatment approaches across multiple cancer types. She has made significant contributions to understanding how imaging technology can improve radiotherapy precision and effectiveness while reducing side effects, with several publications appearing in top journals through 2025. Professor Choudhury has received multiple prestigious awards recognizing her contributions to the field: Cancer Research-UK/Royal College of Radiologists Clinical Training Fellowship (2005) Fellowship for the 10th ECCO-AACR-ASCO Workshop on Methods in Clinical Cancer Research (2007) Outstanding Contribution, Greater Manchester Clinical Research Awards (2017) RCR Research Fellowship (2005) Research Fellowship, Princess Margaret Hospital, Toronto (2004) Professor Choudhury has supervised numerous doctoral and master's students across multiple cancer types, with current students expected to complete through 2024. She is Principal Investigator on multiple research grants, including 'Measuring tumour radioresistance to improve radiotherapy outcomes' and the 'MAESTRO Programme' as part of CRUK RadNet. Her research program is supported by significant funding from NIHR Manchester Biomedical Research Centre and other major funding bodies. As Co-Group Leader of the Translational Radiobiology Group, Professor Choudhury collaborates extensively with leading researchers including Peter Hoskin, Catharine West, Corinne Faivre-Finn, and Marcel van Herk. Her team is at the forefront of integrating advanced imaging with radiotherapy to improve cancer treatment outcomes, with active projects spanning from basic radiobiology to clinical implementation of novel radiotherapy techniques.

• Medical Physics
• Artificial Intelligence in Medicine
• Radiation Therapy Treatment Planning
• +۷ مورد دیگر

Dan Nguyen, Ph.D., is a faculty member in the Department of Radiation Oncology at UT Southwestern Medical Center, where he is part of the Division of Medical Physics and Engineering. He is a founding member of the Medical Artificial Intelligence and Automation (MAIA) Laboratory, collaborating closely with Dr. Steve Jiang to advance AI applications in radiotherapy. His work focuses on deep learning for treatment planning, dose prediction, auto-segmentation, and adaptive radiotherapy. Ph.D. in Biomedical Physics, University of California, Los Angeles (UCLA), 2017 Mentor: Dr. Ke Sheng Faculty appointment at UT Southwestern since 2017 Dr. Nguyen’s research is centered on applying artificial intelligence to solve critical challenges in radiation oncology. His primary interests include deep learning-based dose prediction, auto-segmentation of anatomical structures, optimization of treatment plans, and real-time adaptive radiotherapy. He has pioneered work in direct aperture optimization, 4π radiotherapy, and uncertainty quantification in AI models. His research bridges the gap between AI innovation and clinical implementation, with a focus on improving plan quality, reducing planning time, and enhancing accessibility for less experienced clinicians. The most recent publications (2023–2025) demonstrate a consistent trend in developing fast, accurate, and robust deep learning models for radiotherapy. Key themes include dose prediction with transfer and meta-learning, adaptive segmentation using test-time optimization, uncertainty assessment in AI predictions, and mathematical modeling of radiotherapy-immunotherapy synergy. These works span high-impact journals in medical physics, AI, and oncology, reflecting interdisciplinary innovation. While no specific scientific awards are listed, Dr. Nguyen’s leadership in the MAIA Lab and extensive publication record in top-tier journals indicate significant recognition in the field of medical physics and AI in medicine. Dr. Nguyen has co-authored numerous studies involving mentoring and collaborative research, particularly with trainees and junior faculty in the MAIA Lab. His work is supported by institutional and likely federal funding, given the scale and scope of AI deployment studies. He has contributed to large-scale collaborative efforts such as OpenKBP-Opt, involving international teams evaluating knowledge-based planning pipelines. The MAIA Laboratory is a multi-investigator research group focused on innovating, developing, and applying artificial intelligence technologies to empower clinicians—especially those with less experience or limited resources—for improved patient care. The lab’s work spans machine learning, deep learning, reinforcement learning, and mathematical modeling in radiation oncology.

Yading Yuanمشاهده پروفایل

دانشیار

• Artificial Intelligence in Radiation Oncology
• Medical Imaging Analysis
• Data Science Applications in Healthcare
• +۳ مورد دیگر

Yading Yuan, PhD is an Associate Professor of Radiation Oncology (Physics) at Columbia University Irving Medical Center and a member of the Data Science Institute. He holds a PhD in medical physics from the University of Chicago (2010) and completed clinical residency at Harvard Medical Physics Program (2013). His research focuses on AI-driven innovations in radiation oncology, including automated medical image analysis systems, federated learning frameworks for tumor segmentation, and data-driven approaches to personalized cancer treatment. He is certified by the American Board of Radiology and licensed in New York State. Education: PhD in Medical Physics (University of Chicago, 2010); Clinical Residency (Harvard Medical Physics Program, 2013). Research interests include: automated knowledge-based treatment planning, large-scale clinical AI systems, medical image reconstruction algorithms, and panomics integration for precision oncology. His work emphasizes translating data science advancements into clinical practice to improve patient outcomes. Key trends in his publications include federated learning for privacy-preserving medical AI, tumor segmentation in multi-modal imaging (PET/CT, MRI), and AI-driven prediction of treatment outcomes and recurrence risks. Recent work emphasizes decentralized learning architectures and cross-institutional collaboration systems. Scientific Awards: Distinguished Reviewers 2013 (selected by peer review committees) Advising/grants: No specific student names or grant details listed in provided text. His work is supported through institutional and collaborative research initiatives. Labs/teams: Active member of Columbia's Data Science Institute and Radiation Oncology department, contributing to interdisciplinary medical AI research groups.

Franz Lennard Ricklefsمشاهده پروفایل

استاد

• Neurosurgery
• Neuro-oncology
• Molecular Neurobiology
• +۵ مورد دیگر

Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.

• Radiation Oncology
• Medical Physics
• Cancer Treatment
• +۳ مورد دیگر

Dr Emily Hewson is a Cancer Institute NSW Early Career Fellow and member of the Sydney School of Health Sciences at the University of Sydney's Faculty of Medicine and Health. Her research focuses on advancing real-time radiation therapy techniques, particularly in managing intrafraction motion for prostate and other cancers. She leads projects involving multileaf collimator (MLC) tracking, dose optimization, and deep learning integration in radiation oncology. Research interests include adaptive radiotherapy systems, kilovoltage intrafraction monitoring (KIM), and clinical trial implementation (e.g., TROG 15.01 SPARK trial). Her work emphasizes improving treatment accuracy through real-time dose-guided approaches and multitarget tracking for tumors with complex motion patterns. Developed experimental validations for MRI-linac integration and MLC tracking systems Authored a textbook chapter on Adaptive Radiation Therapy (ART) Recipient of Cancer Institute NSW Early Career Fellowship (2023) Recent grants include an AI platform for targeted radiotherapy (2024) and national critical infrastructure funding for lung cancer applications (2023). Her lab collaborates on real-time dose calculation algorithms and clinical trial implementation across multiple institutions.

Xiaofeng Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

Chad J Brennerمشاهده پروفایل

دانشیار

• Head and Neck Cancer
• HPV-Driven Oncogenesis
• Liquid Biopsy Technologies
• +۵ مورد دیگر

Dr. Chad J. Brenner is an Associate Professor in the Department of Otolaryngology-Head and Neck Surgery and Pharmacology at the University of Michigan Medical School. He also directs the U-M Program in Cellular and Molecular Biology, the Otolaryngology Clinical Laboratory (CLIA), and the Head & Neck Oncology Program. His research focuses on developing liquid biopsy tools for cancer detection and precision therapy, particularly in HPV-driven head and neck cancers. Key roles include membership in the Rogel Cancer Center, Kresge Hearing Research Institute, and Center for Computational Medicine & Bioinformatics. Education: B.S. in Biomedical Engineering, M.S. in Bioelectrical Engineering, and Ph.D. in Cellular and Molecular Biology from the University of Michigan, with doctoral work on prostate cancer mechanisms. Research Interests: HPV integration and cancer heterogeneity Urine- and blood-based liquid biopsies for real-time cancer monitoring Combination immunotherapy strategies for improving checkpoint inhibitor responses Genetic engineering to identify cancer vulnerabilities Clinical trial innovation for adaptive therapies Recent Work Highlights: Development of the MyHPVscore blood test for HPV-related head and neck cancer detection, and exploration of tumor-immune interactions through PD-L1 and T-cell profiling. Ongoing projects include PET-guided radiotherapy optimization and multi-omics analyses of tumor heterogeneity. Labs & Teams: Leads the Michigan Otolaryngology and Translational Oncology (MiOTO) lab and collaborates with interdisciplinary teams across computational medicine, immunology, and clinical oncology.

Haoming Qiuمشاهده پروفایل

دانشیار

• Radiation Oncology
• Gastrointestinal Cancers
• Lung Cancer
• +۵ مورد دیگر

Haoming Qiu, M.D. serves as an Associate Professor in the Department of Radiation Oncology at the University of Rochester School of Medicine and Dentistry. He practices clinically at both Wilmot Cancer Center in Rochester and Sands Cancer Center in Canandaigua, providing radiation oncology services for gastrointestinal, lung, and prostate cancers. Board-certified by the American Board of Radiology, Dr. Qiu specializes in advanced radiotherapy techniques including external beam radiation and radiopharmaceutical therapies. Education: MD, Johns Hopkins University (2011) Residency in Radiation Oncology, University of Rochester Medical Center (2013-2016) Residency in Radiation Oncology, Loyola University Medical Center (2012-2013) Internship in Internal Medicine, Sinai Hospital of Baltimore (2011-2012) Research Focus: Dr. Qiu's work centers on optimizing radiation therapy for gastrointestinal malignancies, prostate cancer, and neuroendocrine tumors. His investigations into theranostics explore novel applications of Lutathera for neuroendocrine tumors, Pluvicto for prostate cancer, and Therasphere for liver cancers. Current research emphasizes combining stereotactic body radiotherapy with immunotherapeutic agents to overcome treatment resistance in pancreatic and rectal cancers. Publication Trends: Recent publications (2023-2025) reveal a strategic shift toward adaptive radiotherapy techniques for pelvic malignancies and immunoradiotherapy combinations. His work demonstrates growing emphasis on modulating tumor immune microenvironments through radiation, particularly in pancreatic and rectal cancers. The integration of mRNA nanotechnology with SBRT represents a cutting-edge frontier in his research portfolio. Scientific Recognition: Roentgen Resident/Fellow Research Award (2015) Excellence in Medical Student Research (2011) Clinical Leadership: Dr. Qiu directs multiple clinical trials at the University of Rochester focusing on radiopharmaceutical applications and adaptive radiotherapy protocols. His patient-centered approach is reflected in consistently high patient satisfaction scores (4.9/5 stars) across communication, empathy, and treatment explanation metrics. Care Coordination: As part of the Wilmot Cancer Center and Sands Cancer Center teams, Dr. Qiu collaborates with multidisciplinary groups including medical oncologists, surgeons, and radiologists to deliver integrated cancer care through Accountable Health Partners network.

Andrew Godleyمشاهده پروفایل

دانشیار

• Radiation Oncology
• Medical Physics
• Radiation Therapy Techniques
• +۳ مورد دیگر

Andrew Godley, Ph.D., is the Associate Vice Chair of Clinical Physics Operations and Quality and an Associate Professor of Radiation Oncology at UT Southwestern Medical Center. He is part of the Department of Radiation Oncology’s Division of Medical Physics and Engineering. Dr. Godley holds a Texas Medical Physics License and is board-certified in therapeutic radiologic physics by the American Board of Radiology. Education: Received his Ph.D. in high-energy physics from the University of Sydney as part of the NOMAD experiment at CERN. Completed a postdoctoral fellowship at the University of South Carolina with the MINOS experiment at FermiLab. Transitioned to medical physics at the Medical College of Wisconsin. Research Interests: Focus on advanced radiation therapy techniques including brachytherapy, SBRT, Gamma Knife, MR-linac integration, and adaptive radiotherapy. His work emphasizes improving treatment accuracy, patient-specific quality assurance, and innovative approaches to personalized oncology care. Publications: Over 118 peer-reviewed articles, with recent work emphasizing adaptive radiotherapy strategies, MR-guided therapies, and computational tools for dose verification. Professional Contributions: Active in developing clinical protocols for radiation physics operations, including machine QA/commissioning and program development for new technologies like MR-linac systems.

Faisal Mahmoodمشاهده پروفایل

استاد

• Medical Image Processing
• Radiation Therapy
• Imaging Biomarkers
• +۷ مورد دیگر

Faisal Mahmood is a Professor at the Department of Clinical Research, University of Southern Denmark (SDU), with dual affiliations at Odense University Hospital (OUH). He is a key member of the Research Unit of Oncology and the AgeCare - Academy of Geriatric Cancer Research in Odense, where he leads advanced research in imaging biomarkers for radiotherapy response. His work bridges clinical oncology and medical physics, with a focus on improving cancer treatment through innovative imaging techniques. Research Interests: Dr. Mahmood's research centers on imaging biomarkers of response to radiotherapy , with expertise in radiation therapy , medical image processing , and diffusion MRI . His work explores tumor microstructure using time-dependent diffusion imaging and MRI-Linac systems, with applications in glioblastoma and pancreatic cancer. He is actively involved in developing low-dose, adaptive radiotherapy protocols to enhance treatment precision. The recent trend in his publications shows a strong focus on adaptive radiotherapy , quantitative MRI , and biologically guided treatment . His work integrates engineering principles with clinical oncology, emphasizing reproducibility and feasibility in clinical settings. Topics such as automatic beam gating, diffusion coefficient discrepancies across scanners, and histological validation of imaging biomarkers reflect his translational research approach. Scientific Awards and Recognition: Research supported by Knæk Cancer foundation Advising and Grants: Dr. Mahmood has supervised at least 3 academic works, including PhD-level research. His projects are supported by institutional and external funding, notably from cancer research foundations. He actively collaborates with multidisciplinary teams across Denmark and internationally, contributing to both national and global oncology research initiatives. Labs and Research Teams: He is affiliated with the Research Unit of Oncology and the AgeCare - Academy of Geriatric Cancer Research at OUH, where he contributes to cutting-edge research in geriatric oncology and advanced radiotherapy. His team integrates clinical data, imaging physics, and machine learning to develop personalized treatment strategies for cancer patients.

• Radiomics
• Medical Imaging Analysis
• Machine Learning in Healthcare
• +۷ مورد دیگر

Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.