Find Academic Supervisors

Jeremy DahlView profile

Professor

• Medical Ultrasound Imaging
• Ultrasonic Beamforming
• Image Reconstruction
• +7 more

Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.

Anne H Schistad SolbergView profile

Professor

• Machine Learning
• Deep Learning
• Image Analysis
• +6 more

Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.

• Biomedical Imaging
• X-ray Computed Tomography
• Phase-Contrast Imaging
• +6 more

Prof. Julia Herzen holds the Associate Professorship of Physics in Biomedical Imaging at the Department of Physics , TUM School of Natural Sciences , Technical University of Munich . Her research focuses on advancing X-ray imaging techniques using synchrotron radiation and laboratory sources, with applications in medical diagnostics and tissue analysis. Position: Associate Professor Department: Physics School: TUM School of Natural Sciences University: Technical University of Munich Contact: julia.herzen@tum.de Her core research interests include: Quantitative multi-modal X-ray imaging (spectral & phase-contrast) 3D virtual histology of human tissue Breast cancer detection improvement Lung disease imaging (emphysema, pneumonia) X-ray phase-contrast tomography Dark-field imaging material decomposition Recent publications demonstrate expertise in dark-field imaging for lung pathology , phase-contrast CT for organoid visualization , and spectral X-ray applications in multi-material differentiation . Her team explores clinical translation of X-ray techniques for non-invasive diagnostics . She supervises PhD students and teaches Biomedical Engineering courses, including: Quantitative X-Ray Imaging (3 VI) Image Processing in Physics (2 VO) Biostatistics (2 VO) Advanced Lab Courses in X-ray Micro-CT

• Biomedical Imaging
• X-ray Computed Tomography
• Spectral Detection
• +3 more

Prof. Julia Hearts is a Professor at the Technical University of Munich (TUM) , affiliated with the School of Natural Sciences . Her research focuses on biomedical imaging , particularly advancing X-ray computed tomography through phase-contrast and dark-field radiography for clinical and biological applications. Developing spectral detection techniques to enhance diagnostic accuracy Quantitative imaging for element-specific parameter extraction Utilizing synchrotron radiation and standard X-ray tubes Her recent publications demonstrate expertise in dark-field radiography for lung and breast imaging, phase-contrast tomography for tissue characterization, and multi-spectral X-ray analysis for material decomposition. Collaborative work spans oncology , pulmonology , and materials science . Contact: julia.herzen@tum.de

Ehsan SameiView profile

Professor

• Medical Imaging Physics
• Radiology
• Computed Tomography (CT)
• +5 more

Ehsan Samei is the Reed and Martha Rice Distinguished Professor of Radiology at Duke University. He holds concurrent professorships in Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering. His leadership roles include Chief Imaging Physicist at Duke University Health System, Director of the Carl E. Ravin Advanced Imaging Laboratories, and Director of the Center for Virtual Imaging Trials (CVIT). Education: University of Michigan (PhD, 1997; MEng, 1995) Key Appointments: Radiology (Clinical Science Departments), Biomedical Engineering (Pratt School of Engineering), Physics (Trinity College of Arts & Sciences) Dr. Samei's research bridges medical imaging physics with clinical applications. His work focuses on photon-counting CT technology, virtual imaging trials, and AI-driven harmonization of CT images. He develops computational models for organ dosimetry, disease quantification, and procedural optimization in radiology. Recent publications emphasize virtual imaging trials for evaluating CT technologies, radiation dose reduction strategies, and AI integration in medical imaging. His studies compare photon-counting CT with conventional systems for lung density, liver lesion detection, and cardiac imaging, while advancing radiomics and dose monitoring frameworks. Scientific Awards Fellow of AAPM (FAAPM) Fellow of SPIE (FSPIE) Fellow of AIMBE (FAIMBE) Fellow of IOMP (FIOMP) Fellow of ACR (FACR) President of AAPM (2023) President of SDAMPP (2010-2011) Dr. Samei has secured major grants from NIH, NCI, and industry partners like GE Healthcare and Siemens. He leads the Center for Virtual Imaging Trials and directs multiple residency training programs in medical physics. His laboratory develops simulation toolkits, 3D-printed phantoms, and dose analytics platforms.

Franz PfeifferView profile

Professor

• X-ray Physics
• Biomedical Imaging
• Computed Tomography
• +6 more

Prof. Dr. Franz Pfeiffer is a full professor at the Chair of Biomedical Physics within the Department of Physics at the Technical University of Munich (TUM) . He has served as director of the Munich School of BioEngineering since 2016. His research focuses on translating advanced X-ray physics concepts to biomedical imaging and clinical applications, particularly for early cancer and osteoporosis diagnostics. Research Interests: X-ray phase-contrast and dark-field imaging, synchrotron instrumentation, CT reconstruction algorithms, and medical imaging technology. Awards: Alfred Breit Prize (2017) ERC Advanced Grant (2016) Leibniz Prize (2011) National Latsis Prize (2010) ERC Starting Grant (2009) His work bridges fundamental X-ray physics with clinical translation, involving collaborations with radiologists, engineers, and medical researchers. Recent publications emphasize AI integration in CT, dark-field chest radiography, and spectral imaging applications.

David CastañónView profile

Professor

• Stochastic Control
• Estimation Theory
• Game Theory
• +6 more

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Thomas KoehlerView profile

Researcher

• Tomographic imaging
• Inverse problems
• Computed tomography
• +3 more

Thomas Koehler is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) , affiliated with the Department of Radiology under the Faculty of Medicine. He leads the Focus Group on Phase Contrast Computed Tomography and collaborates with Philips Research Laboratories in Hamburg. His academic work bridges industry and academia, focusing on advanced imaging techniques. Education : Studied physics at the Christian-Albrechts-University in Kiel (1989–1994), earned a Ph.D. from Philips Research Hamburg (1998). His research spans tomographic imaging, inverse problems, and iterative reconstruction algorithms for CT and phase-contrast X-ray imaging. Key contributions include advancements in helical cone-beam CT and grating-based phase-contrast imaging. Research Interests : Koehler’s work centers on tomographic imaging techniques, phase-contrast X-ray imaging, and medical diagnostics. He has pioneered innovations in dark-field chest imaging for conditions like emphysema and COVID-19 pneumonia. Collaborations with TUM’s Radiology Department drive cutting-edge iterative reconstruction methods. Labs/Teams : Leads the Phase Contrast Computed Tomography Focus Group at TUM-IAS, collaborating with industry partners like Philips. His team develops novel algorithms for medical imaging, emphasizing clinical applicability and hardware optimization.

Cynthia H. McColloughView profile

Professor

• Computed Tomography (CT) Imaging Technology
• Radiation Dose Management
• Material Composition Evaluation via CT
• +3 more

Cynthia H. McCollough, Ph.D., is a Professor of Biomedical Engineering and Medical Physics at Mayo Clinic, where she serves as a Consultant in the Department of Radiology. She leads the CT Clinical Innovation Center, focusing on advancing CT imaging technology and clinical applications. Her work emphasizes radiation dose reduction, quantitative material composition analysis (e.g., kidney stones, gout), and optimizing CT protocols for diagnostic accuracy while minimizing patient risk. Dr. McCollough holds leadership roles in national and international organizations, including the NIH Biomedical Imaging Technology Study Section and the American Association of Physicists in Medicine (AAPM). She has received numerous awards, including the Distinguished Investigator Award from Mayo Clinic and the William D. Coolidge Gold Medal from AAPM. Her research spans photon-counting CT, dual-energy CT, and translational imaging solutions for vascular and abdominal diseases. Dr. McCollough’s academic contributions include over 600 peer-reviewed publications and leadership in grant-funded projects such as the National Institute of Biomedical Imaging and Bioengineering initiatives. Education: PhD in Medical Physics from the University of Wisconsin-Madison, MS in Medical Physics from the University of Wisconsin-Madison, BS in Physics from Hope College. Research Highlights: Her lab develops tools for radiation dose estimation, non-invasive material characterization via CT, and optimizing CT parameters using human and model observers. Recent work includes improving kidney stone analysis, bone mineral density measurements using photon-counting CT, and reducing radiation exposure in pediatric imaging. Grants & Funding: Principal investigator on multiple NIH grants, including projects on coronary atherosclerosis characterization, urinary stone composition analysis, and photon-counting CT for vascular disease detection. Awards: Over 100 awards, including the Distinguished Mayo Clinic Investigator Award (2020), Honorary Doctorate from Linkoping University (2020), and AAPM’s highest honor, the William D. Coolidge Gold Medal (2024). Labs & Teams: Directs the CT Clinical Innovation Center and the X-Ray Imaging Research Core at Mayo Clinic, collaborating with multidisciplinary teams across radiology, biomedical engineering, and physics.

• Applications of AI in Medical Imaging
• Machine Learning
• Dark-Field X-ray Imaging
• +3 more

Tina Dorosti is a researcher at the Technical University of Munich , affiliated with the TUM Faculty of Medicine and the Department of Physics . Her work focuses on applying artificial intelligence to medical imaging, particularly in CT and X-ray technologies. Research Interests: Tina specializes in AI-driven medical imaging solutions, with emphasis on machine learning for disease detection, dark-field X-ray imaging, and spectral X-ray imaging. Her projects address challenges in low-dose imaging, artifact reduction, and lung volume quantification. Publications: Her recent work (2025) includes optimizing CNNs for COPD detection in CT scans, enhancing lung tumor imaging with sparse sampling, and developing deep learning methods for lung volume estimation from chest radiographs. Earlier studies (2024-2021) explore hemorrhage detection, artifact correction, and bone segmentation in clinical imaging. Awards: Cover image of the Radiology: Artificial Intelligence July 2025 issue Collaborations: Tina collaborates with Prof. Franz Pfeiffer and colleagues at the Chair of Biomedical Physics, contributing to interdisciplinary projects in radiology, oncology, and respiratory disease diagnostics.

Anders PerssonView profile

Professor

• Medical Imaging
• Cardiovascular Imaging
• CT Technology
• +3 more

Anders Persson is Professor and Head of the Division of Diagnostics and Specialist Medicine at Linköping University, where he also serves as Director of the Center for Medical Image Science and Visualization (CMIV). His pioneering research develops advanced cardiovascular imaging techniques using photon-counting CT technology and computational fluid dynamics. Professor Persson's innovations include rapid cardiac imaging protocols capturing blood flow in a single heartbeat, AI algorithms for automated coronary calcium scoring, and patient-specific computational modeling of cardiac hemodynamics. His work with SCAPIS (Swedish CArdiopulmonary bioImage Study) examines population-level cardiopulmonary health patterns and atherosclerosis development. Recent publications focus on optimizing photon-counting CT for cardiac applications, reducing metal artifacts from implants, and developing deep learning solutions for cardiac segmentation. His research aims to enable earlier detection of cardiovascular disease through advanced imaging biomarkers. No scientific awards are documented in this profile. Professor Persson leads initiatives translating medical imaging innovations into clinical practice for improved cardiovascular diagnosis.

Julian TachellaView profile

Researcher

• Signal Processing
• Machine Learning
• Computational Imaging
• +5 more

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

Aamir Younis RajaView profile

Assistant Professor

• Medical Physics
• Radiation Physics
• Medical Imaging Physics
• +8 more

Dr. Aamir Younis Raja is an Assistant Professor in the Physics Department at Khalifa University, UAE, where he co-founded the Medical Physics wing and co-developed the accredited M.Sc program in medical physics. He has held academic roles including Senior Research Fellow at the University of Otago, Visiting Research Fellow at the University of Canterbury, and Visiting Academic Teaching Staff at ARA Institute of Canterbury. His research focuses on radiation physics , medical imaging physics , and spectral photon-counting CT applications in bone/cartilage health , metal implant characterization , cancer imaging , atherosclerosis , and arthritis . Education: PhD in Medical Physics (University of Canterbury, 2013), M.Sc in Applied Physics (UET Pakistan, 2006), B.Sc in Physics & Mathematics (University of the Punjab, 2004) Dr. Raja’s work combines nanoparticle technology with low-dose multi-energy CT to identify non-toxic contrast agents, develops machine learning-based radiation monitoring tools , and pioneers AI-driven artefact reduction in CT imaging. His projects include collaborations with international institutions on material decomposition algorithms and biomedical applications of spectral CT. Notable scientific recognition includes being a Fellow of the Union for International Cancer Control and securing the Khalifa University Faculty Startup Grant . He has supervised over 20 thesis students across University of Otago, University of Canterbury, and Khalifa University, including PhD candidates working on material reconstruction software , parametric color imaging , and machine learning for CT artefact reduction .

PAOLO CASTELLINIView profile

Associate Professor

• Mechanical Measurements
• Vibration Analysis
• Hyperspectral Imaging
• +4 more

Paolo Castellini is an Associate Professor at the Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche (UNIVPM). His research focuses on mechanical measurements, vibration analysis, and advanced imaging techniques. He teaches courses in the College of Engineering and has published extensively on applications of hyperspectral imaging, 3D scanning, and neural networks in industrial and biomedical contexts. Office hours: Monday-Friday, 08:30-13:00 Email: p.castellini@univpm.it Location: via Brecce Bianche, 12, Ancona Recent research trends include vibration decoupling metastructures, microplastic detection in biological samples, and UAV-based photogrammetric analysis of olive trees. His publications emphasize non-destructive testing, acoustic beamforming, and uncertainty evaluation in measurement systems. Notable projects involve mmWave radar displacement analysis, cardiac simulators for prosthetic valves, and light-controlled polymer films for mechanical motion.

David Peter CormodeView profile

Professor

• Nanomedicine
• Medical Imaging
• Radiology
• +7 more

David Peter Cormode is a full Professor in the Department of Radiology at the Perelman School of Medicine, University of Pennsylvania. He is affiliated with multiple translational research centers including the Center for Translational Targeted Therapeutics and Nanomedicine, Institute for Translational Medicine and Therapeutics, and the Penn Institute for Immunology. His research focuses on nanoparticle-based contrast agents for CT, MRI, and molecular imaging , with applications in cardiovascular disease and cancer diagnostics . Key innovations include developing gold, bismuth, and cerium oxide nanoparticles with fluorescence, antioxidant, and therapeutic properties . Recent publications highlight advances in biofilm infection treatment (2024), photon-counting CT (2023), and renally excretable contrast agents (2022). His work bridges chemistry, radiology, and biomedical engineering through collaborations with the Penn Bioengineering and Pharmacology Graduate Groups.