Find Academic Supervisors

Yves BourgaultView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods
• Finite Element Methods
• +3 more

Yves Bourgault is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a MSc and PhD from Laval University. His research focuses on computational fluid dynamics, numerical methods, finite element techniques, and continuum mechanics modeling, with applications in cardiac electrophysiology and ecological systems. Dr. Bourgault has supervised several graduate students, including Edward Boey (co-supervised), Sana Keita, Saint-Cyr Koyagurebo-Ime, and Kak Choon Loy. His work integrates advanced numerical techniques to address complex problems in biomedical engineering, environmental science, and mathematical physics. Key methodologies include finite element methods, deferred correction schemes, and anisotropic mesh adaptation. His research group is part of the Applied Mathematics division at the University of Ottawa, emphasizing interdisciplinary applications. Recent work explores climate change impacts on ecological systems, cardiac tissue modeling using high-resolution MRI data, and robust numerical methods for reaction-diffusion equations. Publications span topics such as bidomain models for cardiac electrophysiology, fluid-structure interaction in heart mechanics, and mathematical modeling of fuel cells. His contributions bridge theoretical numerical analysis with real-world biomedical and environmental challenges.

Benjamin de HaasView profile

Researcher

• Visual Perception
• Face Processing
• Individual Differences
• +3 more

Dr. Benjamin de Haas is a vision scientist and faculty member at Justus Liebig University Giessen , Germany, within the Department of Psychology and Sports Science . He currently leads the ERC-funded Indivisual project and co-leads project C9 Factors influencing categorical face processing within the Collaborative Research Centre CRC/TRR 135. He is also a principal investigator in the NeurOscientific Workflow Assistance (NOWA) infrastructure project, dedicated to open, reproducible neuroscience. Research Focus Dr. de Haas pursues two intertwined questions: How do early and late stages of visual processing interact—from the initial registration of slanted edges to the recognition of faces? How and why do our perceptions differ from one person to the next? To answer these questions his group combines psychophysics, high-resolution eye-tracking, functional and quantitative MRI, and computational modelling, with a strong emphasis on face perception, individual differences, and naturalistic viewing conditions. Publications Overview Across more than 20 publications since 2016, Dr. de Haas has advanced understanding of individual differences in face processing, gaze control, and visual salience. His work repeatedly appears in Journal of Vision , Nature Communications , PNAS , and NeuroImage , highlighting a sustained focus on eye-movement behaviour, cortical representations of faces and scenes, and methodological best practices in neuroimaging. Current Supervision & Team Dr. de Haas currently supervises two PhD students: Elaheh Akbarifathkouhi Hilal Nizamoglu Together with Dr. Katharina Dobs (co-project leader) and affiliated post-docs and research technicians, the group forms the Indivisual laboratory at Giessen. Contact & Resources Email: Benjamin.de-Haas@psychol.uni-giessen.de Department of Psychology and Sports Science Otto-Behaghel-Str. 10F, 35394 Gießen, Germany

Yang SongView profile

Assistant Professor

• Generative Models
• Diffusion Models
• Score-Based Modeling
• +5 more

Yang Song is an incoming Assistant Professor in Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Prior to joining Caltech, he leads the Strategic Explorations team at OpenAI. He received his Ph.D. in Computer Science from Stanford University under the supervision of Stefano Ermon and completed his Bachelor's degree in Mathematics and Physics from Tsinghua University. His educational background includes: Ph.D. in Computer Science, Stanford University Bachelor's in Mathematics and Physics, Tsinghua University Dr. Song's research focuses on building powerful AI models capable of understanding, generating, and reasoning with high-dimensional data across diverse modalities. He is particularly known for inventing foundational concepts and techniques in score-based diffusion models, which have revolutionized the field of generative AI. His work bridges theoretical advances with practical applications, particularly in image generation, medical imaging, and solving inverse problems. His research has demonstrated how score-based models can achieve state-of-the-art results in image generation while maintaining flexibility for various applications including medical image reconstruction. Analysis of his publication record reveals a strong trajectory in generative modeling, with a particular emphasis on score-based approaches and diffusion models. His work consistently addresses fundamental challenges in generative modeling including sample quality, training stability, computational efficiency, and application to real-world problems. His most recent work on consistency models represents a significant advancement toward making generative models practical for real-time applications. His notable achievements include: ICLR 2021 Outstanding Paper Award for Score-Based Generative Modeling through Stochastic Differential Equations NeurIPS 2021 Spotlight Presentation for Maximum Likelihood Training of Score-Based Diffusion Models Multiple ICLR Oral presentations for his work on consistency models Developing foundational techniques that power many modern AI image generation systems His GitHub repository for score-based generative modeling has gained significant traction in the research community, with over 1,700 stars, reflecting the impact of his work. His research bridges theoretical machine learning with practical applications, particularly in medical imaging where his techniques have shown promise for improving image reconstruction in CT and MRI.

Douglas C. NollView profile

Professor

• Medical Imaging
• Biomedical Engineering
• Functional MRI (fMRI)
• +5 more

Douglas C. Noll is the Ann and Robert H. Lurie Professor of Biomedical Engineering and Professor of Radiology at the University of Michigan. He holds key roles as Co-Director of the Functional MRI Laboratory, Co-Lead of the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center, and collaborator at the Michigan Institute for Imaging Technology and Translation (MIITT). His affiliations include the Michigan Neuroscience Institute, Center for Computational Medicine and Bioinformatics, and Michigan Concussion Center. His research focuses on advancing MRI and fMRI technologies to study brain function and neurological disorders. Key projects include rapid image acquisition, artifact elimination, physiological modeling, and MRI-guided therapies like histotripsy. Recent work emphasizes pre-clinical MRI-guided focused ultrasound systems and collaborations with neuroscientists to map brain organization in health and disease. Notable contributions include developing the Oscillating Steady State Imaging (OSSI) technique, the TOPPE framework for MRI sequence prototyping, and tools like FieldMapNet MRI for off-resonance correction. His lab addresses challenges in high-resolution fMRI, real-time motion compensation, and translational imaging for clinical applications. Current efforts span improving MRI hardware-software integration, advancing non-invasive brain therapies, and applying machine learning to enhance image reconstruction and artifact correction. Collaborations bridge engineering, neuroscience, and clinical medicine to tackle complex neurological conditions like Alzheimer’s and brain tumors.

Zhaolin ChenView profile

Associate Professor

• Machine Learning
• Deep Learning
• Medical Imaging
• +5 more

Zhaolin Chen is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He holds a PhD in Biomedical Imaging from Monash University and has held roles at the University of Melbourne, Florey Neuroscience Institutes, and the medical imaging industry in Europe. He is an Australian Research Council MCR Industry Fellow and leads Australia's first Point-of-Care MRI network at the National Imaging Facility. His research focuses on AI-driven medical imaging, MRI/PET methods, and multimodal data analysis. He has secured over $8M in research funding, including leadership roles in major projects like the National Mobile MRI Network. Education: PhD in Biomedical Imaging, Monash University Research Fellowships at University of Melbourne and Florey Neuroscience Institutes Research Interests: Deep learning and machine learning in medical imaging MRI/PET acquisition/reconstruction methods Multimodal imaging (e.g., simultaneous MR-PET) Translational research with 10+ patents (5 commercialized) Awards & Grants: ARC Discovery Project (Primary Chief Investigator) 5 highly cited papers (top 10% worldwide in 2021) 2021 SciVal: 90% publications in top 10% journals Recipient of Douglas Lampard Research Prize, ISMRM Magna Cum Laude Leadership & Service: President-Elect, ANZ Chapter of ISMRM (2024) Associate Editor for IEEE ISBI (2022-2023) Program Committee Member for ISMRM (2018-2021) Labs & Teams: Monash Biomedical Imaging leadership National Mobile MRI Network project leadership Collaborations across global institutions (e.g., Hyperfine Inc., University of Queensland)

Jordan TheriaultView profile

Assistant Professor

• Neuroscience
• Cognitive Science
• Neuroimaging
• +3 more

Jordan Theriault is an Assistant Professor at Northeastern University, affiliated with both the Biology and Psychology departments. His research focuses on understanding the brain as a self-regulating system, particularly exploring brain-based metabolic costs of information encoding and their implications for mental health and neuroimaging interpretation. He employs advanced neuroimaging technologies like 7 Tesla MRI and simultaneous PET/MR imaging to study brain metabolism and functional changes in critical regions like the brainstem and hypothalamus. Dr. Theriault’s theoretical work emphasizes the brain’s role in predicting sensory input and regulating bodily states, proposing that predictable environments enhance metabolic efficiency. This framework has implications for mental/physical health and social dynamics, such as conformity and social pressure. His lab, the Interdisciplinary Affective Sciences Laboratory, integrates neuroscience, psychology, and philosophy to address complex questions about emotion, morality, and interoceptive control. Key technical innovations include high-resolution imaging techniques and methodological critiques of brain-behavior relationships. His work bridges basic research with applied insights into human behavior, leveraging interdisciplinary approaches to advance understanding of neural systems and their societal relevance.

Tommy LöfstedtView profile

Associate Professor

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +4 more

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

• Particle Astrophysics
• Gamma-ray Astronomy
• Cosmic Ray Physics
• +3 more

Corbin E. Covault serves as Professor and Chair of the Department of Physics at Case Western Reserve University (CWRU), leading experimental research in particle astrophysics and cosmic ray physics through major international collaborations including the Pierre Auger Observatory and Cherenkov Telescope Array (CTA). B.A., Massachusetts Institute of Technology (1985) Ph.D., Harvard University (1991) Covault's research centers on experimental particle astrophysics with emphasis on ultra-high energy cosmic rays, gamma-ray astronomy, and advanced instrumentation. His group develops photodetection systems, GPS timing solutions, and wireless communications for cosmic ray detectors. Key projects include investigating macroscopic dark matter using Auger fluorescence telescopes, designing a 100+ meter diameter 'flat' telescope for exoplanet detection, and pioneering spectral CT imaging with quantum dot X-ray detectors. His instrumentation work directly impacts next-generation observatories like CTA, where his team deploys silicon photomultiplier camera systems. Analysis of Covault's 15 most recent publications (2016-2019) reveals dominant research trends in cosmic ray mass composition, anisotropy studies, and multi-messenger astrophysics. His work consistently bridges particle physics and astrophysics through ultra-high energy cosmic ray observations, neutrino follow-ups of gravitational wave events, and development of novel detection methodologies. Key thematic areas include hadronic interaction modeling at extreme energies, radio-based cosmic ray detection, and searches for exotic particles like magnetic monopoles. Covault leads the CWRU group in the Pierre Auger Collaboration as a member of its 15-person Technical Board, overseeing instrument performance and data integrity. His group received MRI grant funding for CTA camera development starting August 2018, focusing on GPS timing synchronization and rapid trigger processing. Collaborative projects include spectral CT imaging with Philips and Wayne State University, optical SETI telescope development, and macro dark matter investigations with Glenn Starkman. His laboratory activities center on the Auger and CTA observatories, with instrumentation specialties in photodetection systems, GPS timing networks, and wireless data transfer. The CWRU group maintains active roles in Auger's science analysis teams and CTA's prototype deployment at Mt. Hopkins Observatory, Arizona.

Florian KnollView profile

Professor

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +3 more

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

Cornelius FaberView profile

Professor

• MRI cell tracking
• infection imaging
• MR methodology
• +6 more

Cornelius Faber is a University Professor in the Department of Radiology at the University of Münster, Germany, where he leads the Experimental Nuclear Magnetic Resonance research group. His work focuses on developing and implementing novel MRI techniques that extend the boundaries of magnetic resonance imaging in terms of spatial and temporal resolution, sensitivity, and specificity for physiological, structural, and molecular changes. He actively participates in the "Cells in Motion" interdisciplinary research initiative at the university. Professor Faber's research spans multiple critical areas in medical imaging and biomedical science. His primary expertise lies in MRI cell tracking , enabling visualization of cellular dynamics in vivo. He has made significant contributions to infection imaging , developing methods to detect and characterize microbial infections using MRI. His work on MR methodology development has advanced quantitative imaging techniques, while his research on multimodal integration in MR and MRI contrast mechanisms has provided deeper insights into molecular and cellular processes. His research bridges physics, engineering, and biomedical applications, with particular relevance to inflammation, cancer, neurological disorders, and cardiovascular disease. Analysis of Professor Faber's extensive publication record reveals a clear evolution from fundamental MRI technique development toward increasingly sophisticated applications in disease models. His recent work demonstrates a strong trend toward multimodal imaging approaches that combine MRI with complementary techniques such as mass spectrometry, optical imaging, and PET. This integration creates comprehensive diagnostic platforms that provide both anatomical and molecular information. A notable pattern is the focus on cellular dynamics, particularly immune cell behavior in inflammatory conditions and tumor microenvironments, with applications spanning neuroscience, oncology, and cardiology. Professor Faber leads a multidisciplinary research team of approximately 15 members, including scientists, doctoral students, technicians, and medical students. His laboratory is deeply integrated with the University of Münster's research infrastructure, particularly the Multiscale Imaging Centre. The group's work contributes significantly to advancing preclinical MRI methodologies while maintaining strong clinical relevance, with numerous publications in high-impact journals across medical imaging, neuroscience, and biomedical engineering disciplines.

Pratip K. BhattacharyaView profile

Associate Professor

• Magnetic Resonance Imaging (MRI)
• Hyperpolarized MRI
• Metabolic Imaging
• +9 more

Pratip K. Bhattacharya, Ph.D. , is an Associate Professor in the Department of Cancer Systems Imaging and the Department of Imaging Physics at The University of Texas MD Anderson Cancer Center, with a joint appointment in the Graduate School of Biomedical Sciences at The University of Texas Health Science Center. He is a principal investigator leading the Bhattacharya Laboratory, dedicated to advancing magnetic resonance imaging (MRI) through hyperpolarization techniques for applications in cancer and cardiovascular diseases. His research focuses on developing real-time metabolic and molecular imaging methods using hyperpolarized 13 C and 15 N-labeled compounds and silicon nanoparticles. These innovative probes significantly enhance MRI sensitivity, enabling non-invasive assessment of tissue metabolism and targeted imaging. His lab's work spans three primary areas: real-time metabolic MR imaging, targeted molecular MR imaging with functionalized silicon nanoparticles, and high-resolution MR metabolomics. These efforts are aimed at improving disease diagnosis and therapy monitoring. Analysis of his recent publications reveals a strong and consistent focus on hyperpolarized MRI, particularly using silicon particles and metabolic tracers like succinate, to visualize cancer metabolism and cardiovascular conditions in vivo. His research integrates physics, chemistry, and biomedical engineering to create novel imaging tools with direct clinical translational potential. PHIP Hyperpolarization Dynamic Nuclear Polarization (DNP) Hyperpolarized Silicon Nanoparticles Real-Time Metabolic Imaging Cancer and Cardiovascular Imaging Theranostic Applications Dr. Bhattacharya actively mentors graduate students and postdoctoral fellows, including Saleh Ramezani, Jose Enriquez, Dontrey Bourgeois, and Kang-Lin Hsieh. He collaborates closely with physician-scientists, radiologists, and oncologists to ensure his imaging science innovations address critical clinical needs. His laboratory is supported by grant funding, facilitating the development of cutting-edge imaging technologies. The Bhattacharya Laboratory is a key component of the Division of Diagnostic Imaging at MD Anderson, fostering a collaborative environment for interdisciplinary research. The lab focuses on translating fundamental discoveries in hyperpolarization physics into practical tools for improving cancer care.

Wendy MeiringView profile

Professor

• Statistical Methods for Brain Imaging
• Spatial and Temporal Processes
• Computational Statistics
• +4 more

Wendy Meiring is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research focuses on statistical methods for analyzing complex data in neuroscience, environmental science, and biomedical applications. She specializes in spatial and temporal processes, computational statistics, machine learning, and uncertainty quantification. Her work integrates advanced statistical techniques with real-world challenges, such as analyzing brain imaging data, pharmacokinetic models, and environmental monitoring. She has contributed to methodologies for functional data analysis, clustering-based correlation estimation, and spatial-temporal modeling. Dr. Meiring has published extensively in top journals, including The Journal of Computational and Graphical Statistics , focusing on topics likeFréchet regression, pharmacokinetic modeling, and environmental phenology. Her research bridges theoretical statistics with practical applications in health, neuroscience, and ecology. She collaborates across disciplines, contributing to programs like the Interdepartmental Graduate Program in Dynamical Neuroscience at UCSB. Her lab develops innovative statistical tools for analyzing high-resolution datasets, emphasizing reproducibility and methodological rigor.

Markus HaltmeierView profile

Professor

• Image Reconstruction
• Inverse Problems
• Deep Learning
• +4 more

Markus Haltmeier is a Professor in the Department of Mathematics at the University of Innsbruck. His research focuses on inverse problems, image reconstruction, and deep learning with applications in medical imaging, photoacoustics, and computational mathematics. He leads a group dedicated to advancing theoretical and practical solutions for challenges in non-destructive testing and medical diagnostics. His work integrates mathematical analysis with machine learning, addressing issues such as high-resolution imaging in scattering media and automated segmentation of cardiac structures. Key research areas include regularization techniques for inverse problems, self-supervised learning approaches for limited data scenarios, and computational methods for photoacoustic tomography. His contributions span both theoretical developments (e.g., inversion formulas for Radon transforms) and applied solutions (e.g., algorithms for cylinder liner wear assessment and myocardial infarct segmentation). Publications highlight advancements in neural network-based regularization, 3D medical image synthesis, and unsupervised learning frameworks for segmentation and registration. His research emphasizes bridging the gap between mathematical theory and real-world applications in healthcare and engineering.

Philbert TsaiView profile

Academic

• Quantitative Biology
• Neurovascular Imaging
• Biomedical Engineering
• +3 more

Philbert Tsai is an Associate Teaching Professor in the Department of Physics at the University of California, San Diego (UCSD). He has held roles as QBio Lab Coordinator/Project Scientist (2015–Present) and Associate Project Scientist (2011–2015), overseeing advanced laboratory setups and bio-imaging research projects. His work focuses on neurovascular systems, microscopy techniques, and cortical blood flow dynamics. Education: Ph.D., Physics, UC San Diego, 2004 Research Interests: Quantitative analysis of cortical microvascular networks Development of ultra-high-resolution imaging systems (e.g., STED, two-photon microscopy) Neurovascular coupling mechanisms and their impact on brain oxygen supply Biomedical engineering applications in neuroscience research Lab & Projects: QBio Lab: Advanced instrumentation including confocal microscopes, 3D printers, and wet-lab equipment Developed vectorized models of mouse brain vasculature and ultra-wide-field multiphoton imaging systems Grants & Awards: No specific awards listed in provided text Collaborations: Worked extensively with colleagues like Dr. David Kleinfeld and Dr. Berislav Zlokovic on neurovascular projects.

Jörg WrachtrupView profile

Professor

• Quantum Sensing
• Diamond Quantum Sensors
• Nitrogen-Vacancy Centers
• +6 more

Jörg Wrachtrup is a Professor at the Department of Physics, Faculty of Mathematics and Physics, University of Stuttgart, and a Fellow at the Max Planck Institute for Solid State Research. He is recognized as one of the most cited physicists globally due to his groundbreaking work on diamond-based quantum sensors, particularly utilizing nitrogen-vacancy (NV) centers for ultra-precise measurements. His research focuses on advancing quantum sensing technologies with applications in neuroscience, medical diagnostics (e.g., next-generation MRI and NMR), and materials science. These sensors enable non-invasive, high-resolution detection of magnetic fields at the nanoscale, opening new frontiers in both fundamental physics and real-world technology deployment. Wrachtrup emphasizes interdisciplinary collaboration and early engagement with applied sciences and industry, advocating for partnerships between academia, Fraunhofer institutes, startups, and large enterprises to accelerate the commercialization of quantum technologies. He believes quantum sensing is ahead of quantum computing in terms of practical application and near-term impact. Scientific awards and recognitions are not mentioned in the provided text. He actively mentors students and leads a research group focused on quantum sensor development, though specific advisees are not listed. His lab collaborates closely with institutions like the Fraunhofer IAF, which develops diamond-based semiconductor qubits and sensor platforms. The team works on translating fundamental quantum phenomena into scalable, deployable technologies, with future goals including miniaturized, high-sensitivity sensors for integration into medical devices and potentially consumer electronics.