جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Medical Physics
• Therapeutic Ultrasound
• +۴ مورد دیگر

Douglas Christensen is a Professor in both the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering at the University of Utah. His academic career spans over five decades, beginning with a Ph.D. in Electrical Engineering from the University of Utah in 1967. He has authored or co-authored multiple books, including Ultrasonic Bioinstrumentation and Basic Introduction to Bioelectromagnetics . Education: BS, Electrical Engineering, Brigham Young University (1962) MS, Electrical Engineering, Stanford University (1963) PhD, Electrical Engineering, University of Utah (1967) Postdoctoral Fellowship, University of Washington (1972-1974) Research Interests: Therapeutic ultrasound Optical biosensors Acoustic radiation force Phase aberration correction 3D MR thermometry Medical imaging systems Ultrasound wave propagation Scientific Awards: Fellow, American Institute for Medical and Biological Engineering (2001) Distinguished Teaching Award, University of Utah (2004) Lifetime Achievement Award, ECE Department (2020) ECE Chair's Award (2022) His recent publications focus on improving transcranial ultrasound simulations through skull geometry modeling, optimizing phase aberration correction for breast MRgFUS, and enhancing MR thermometry accuracy via model predictive filtering. He has secured significant NIH grants for HIFU bioengineering and transcranial MRgFUS innovations. Christensen teaches foundational courses in biomedical engineering and electrical circuits, with active roles in 2023-2024 academic terms.

• Radiology
• Emergency Radiology
• Neuroradiology
• +۳ مورد دیگر

Dr. Vaishali Kapare is an Assistant Professor at UMass Chan Medical School’s Department of Radiology in Worcester, Massachusetts. Her work bridges Radiology , Emergency Medicine , and Artificial Intelligence in medical imaging, with a focus on neuroimaging and diagnostic accuracy. Education : MBBS from Dr DY Patil Medical College Hospital and Research Centre, Pune, India. Her research explores emphysematous osteomyelitis , Rathke's Cleft Cyst resolution , and AI-driven segmentation of high-grade gliomas using MRI. These publications highlight cross-disciplinary work in Emergency Radiology , Neuroradiology , and AI applications for volumetric analysis in oncology. Kapare’s co-authors include researchers like Debajyoti Saha and Ganesh Joshi. Her work is tied to concepts such as Pyelonephritis , Tomography , and middle-aged patient care , reflecting her clinical and technical expertise.

Ayan Senguptaمشاهده پروفایل

استادیار

• Neuroimaging
• Functional magnetic resonance imaging (fMRI)
• Ultra-high-field 7 T MRI
• +۵ مورد دیگر

Ayan Sengupta, Ph.D. is an Assistant Professor in the Department of Health Physics and Diagnostic Sciences at the University of Nevada, Las Vegas , where he began in Fall 2024. He teaches Comprehensive Medical Imaging , MRI Physics , Radiation Science , and Dosimetry for both undergraduate and graduate programs. Educational Background Bachelor’s degree in Engineering (India) Master’s degree in Computer Science, specializing in medical image processing, University of Nebraska Ph.D. in Neuroimaging, Otto-von-Guericke University, Germany (supported by DFG Research Scholarship) Postdoctoral research, University of Cambridge & Sir Peter Mansfield Imaging Centre, United Kingdom Lifetime Honorary Affiliate, Hughes Hall, University of Cambridge Research Fellow, Royal Holloway, University of London Research Focus Dr. Sengupta’s work centers on cutting-edge neuroimaging, particularly ultra-high-field 7 T human fMRI . He leverages machine learning and artificial intelligence to decode how the brain represents visual and tactile information. At UNLV he is establishing a program on accessible low-field MRI systems for primary care and is pioneering novel imaging and AI techniques for dementia biomarkers . Publication Landscape Across 20 peer-reviewed works, a clear arc emerges: pioneering 7 T fMRI methodologies (motion correction, resolution optimization), constructing probabilistic atlases of somatosensory digit maps, and integrating computational approaches (decoding orientation, musical genres). Recent efforts extend to low-field MRI accessibility and AI-driven dementia biomarkers , reflecting a translational trajectory from high-field neuroscience to clinical impact. Scientific Honors DFG (Deutsche Forschungsgemeinschaft) Research Scholarship Lifetime Honorary Affiliate, Hughes Hall, University of Cambridge Current Activities & Future Directions Dr. Sengupta is actively building his laboratory at UNLV, acquiring funding and collaborators to advance low-field MRI systems and AI-based dementia diagnostics. While no specific students are named in the provided text, his program is expected to recruit graduate researchers as infrastructure develops.

Paul Yushkevichمشاهده پروفایل

استاد

• Biomedical Imaging
• Neuroscience
• Neurodegenerative Diseases
• +۳ مورد دیگر

Paul Yushkevich is a Professor in the Department of Radiology at the University of Pennsylvania and a member of the Bioengineering Graduate Group. His research focuses on computational methodologies for biomedical imaging data, particularly automatic segmentation and morphometry of the hippocampal formation in Alzheimer’s disease. Education: PhD in Computer Science from University of North Carolina Chapel Hill His work bridges machine learning and neurodegenerative disease research, with recent publications emphasizing MRI analysis, tau pathology, and longitudinal biomarker studies in Alzheimer’s disease. Dr. Yushkevich develops tools for automated segmentation of brain structures and investigates the interplay between neurodegeneration, cerebral blood flow, and cognitive decline. Current research trends include: High-resolution MRI segmentation of medial temporal lobe subregions Quantitative tau burden metrics (e.g., Tau-MaX) Neural correlates of preclinical Alzheimer’s Integration of plasma biomarkers with structural imaging Postmortem MRI-histology validation protocols Dr. Yushkevich’s contributions span Alzheimer’s disease, dementia subtyping, and open-source medical image analysis tools.

Stephen Harwardمشاهده پروفایل

استادیار

• Neurosurgery
• Neurobiology
• Epilepsy
• +۳ مورد دیگر

Stephen Harward is an Assistant Professor of Neurosurgery and Neurobiology at Duke University School of Medicine, with clinical and research expertise in advanced neurosurgical interventions. His work focuses on translating innovative technologies like magnetic resonance-guided focused ultrasound into clinical practice for movement disorders and epilepsy. His academic foundation includes: B.A. from Duke University (2006) Ph.D. in Neurobiology from Duke University School of Medicine (2015) M.D. from Duke University School of Medicine (2016) Neurosurgery Residency at Duke University (2016-2023) Dr. Harward's research integrates clinical neurosurgery with basic neuroscience , specializing in focused ultrasound applications for essential tremor and epilepsy, neuromodulation techniques for psychiatric conditions, and molecular mechanisms of synaptic plasticity. His lab employs cutting-edge approaches including high-resolution neural recording and tractography to refine surgical targeting and develop non-invasive therapies. Analysis of his publication trajectory reveals accelerating contributions to focused ultrasound methodology (40% of recent work), with growing emphasis on pediatric applications and neuropsychiatric indications. His team consistently bridges engineering innovations with clinical implementation, particularly in optimizing perioperative protocols for complex cases. Current research support includes: Neurobiology Training Program (2024-2029) Medical Scientist Training Program (2022-2027) Prior funding for synaptic plasticity (2013-2016) and TrkB receptor studies (2012-2015) He mentors students through NEUROSCI 493 independent study courses and collaborates extensively with Duke's Neurosurgery, Neurobiology, and Radiology departments on translational projects targeting neurological disease mechanisms.

• Medical Imaging
• Biomedical Engineering
• Cancer Research
• +۳ مورد دیگر

Dr. Gene Kim is a Professor of Biomedical Engineering in Radiology at Weill Cornell Medicine . His research focuses on developing advanced magnetic resonance imaging (MRI) techniques for cancer diagnosis and treatment monitoring, particularly in breast and head/neck cancers. The Kim Laboratory investigates tumor microenvironment through dynamic contrast-enhanced (DCE-MRI) and diffusion MRI (dMRI) methodologies. Ph.D. in Biomedical Engineering, University of Southern California Postdoctoral Training in Cancer Imaging, University of Pennsylvania Key research areas include: Tumor vascular properties and water exchange kinetics Quantitative dMRI for cell viability assessment Adipose-tissue interactions in breast cancer Development of active contrast encoding (ACE-MRI) for comprehensive imaging Recent publications demonstrate expertise in high-resolution 3D imaging , deep learning applications , and temporal resolution optimization . The lab has received continuous funding from the National Cancer Institute through grants like R01CA219964 and UG3/UH3CA228699 . Notable methodological contributions include: Golden-angle radial sparse parallel (GRASP) MRI POMACE framework for cell size measurement ACE-MRI for integrated T1/flip angle quantification

Moritz Herrmannمشاهده پروفایل

پژوهشگر

• Empirical Machine Learning
• Manifold Learning
• Representation Learning
• +۵ مورد دیگر

Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .

• Neuroradiology
• Magnetic Resonance Imaging
• Neuroscience
• +۵ مورد دیگر

Professor Tarek Yousry is a distinguished Professor of Neuroradiology at University College London's Institute of Neurology, serving in the Department of Translational Neuroscience and Stroke at the Queen Square campus in London, WC1N 3BG, United Kingdom. His research program focuses on advancing magnetic resonance imaging techniques to improve diagnosis and treatment of central nervous system disorders through sophisticated imaging approaches. Professor Yousry's primary research interests center on two major clinical domains: brain tumors (particularly low-grade gliomas) and neuromuscular diseases. In neuro-oncology, he leverages the Angio-MR intraoperative suite at the National Hospital for Neurology and Neurosurgery to develop comprehensive functional MRI protocols for motor and language mapping, optimize diffusion tensor imaging for white matter tract identification, and create atlas-based methods for residual tumor volume assessment. His neuromuscular research within the MRC Centre for Neuromuscular Disease aims to establish MRI as a novel diagnostic tool for peripheral nerve lesions and a sensitive method for monitoring disease progression and therapeutic responses. His recent publication record (2024-2025) demonstrates significant contributions across multiple domains including multiple sclerosis imaging, quantitative MRI biomarkers, structured reporting systems, and contrast agent usage protocols. Professor Yousry's work spans brain tumors, multiple sclerosis, amyotrophic lateral sclerosis, and peripheral nerve disorders, with numerous high-impact publications in journals like European Radiology, Brain, and Neurology. Professor Yousry maintains extensive collaborations across University College London, including the Welcome Trust Centre for Neuroimaging, the Institute of Cognitive Neurosciences, and the UCL Centre for Medical Image Computing. His international collaborations are evident through participation in MAGNIMS initiatives and European research consortia focused on multiple sclerosis and neuroimaging standards. His research has practical clinical applications, as demonstrated by his evaluation of stroke imaging software and work on optic nerve MRI for multiple sclerosis management.

• Condensed Matter Physics
• Experimental Physics
• Quantum Spin Dynamics
• +۳ مورد دیگر

Merideth Frey is a Professor of Physics at Sarah Lawrence College. Her research focuses on experimental nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI), with a particular emphasis on extending these technologies to solids and biomedical applications. She is actively involved in physics education research, aiming to integrate computation and inquiry-based learning into undergraduate curricula.

• Cognitive Modeling
• Human-centered Artificial Intelligence
• Applied Perceptual Decision Making

Martijn Mulder is an Assistant Professor at the Department of Experimental Psychology within the Social and Behavioral Sciences school at Utrecht University. His research focuses on understanding how humans process information under constraints in time, knowledge, and data to make decisions in daily life, with a particular emphasis on applied perceptual decision making and cognitive modeling . He is also involved in multidisciplinary projects combining cognitive neuroscience and high-resolution MRI, such as the STW Atlasing project. Expertise in cognitive modeling and human-centered AI Active in teaching and course development Collaborates on tactile perception research

Yogesh Rathiمشاهده پروفایل

دانشیار

• Magnetic Resonance Imaging (MRI)
• Diffusion MRI
• Medical Imaging
• +۷ مورد دیگر

Dr. Yogesh Rathi is an Associate Professor of Psychiatry and Radiology at Harvard Medical School and Brigham and Women's Hospital. His research focuses on computational magnetic resonance imaging (MRI) techniques to analyze brain structure and function, particularly in psychiatric and neurological disorders. Associate Professor of Psychiatry and Radiology Brigham and Women's Hospital Harvard Medical School His research spans advanced diffusion MRI for faster imaging, ultra-high-resolution tractography, and harmonization of multi-scanner MRI data. He applies these methods to study white matter connectivity in humans and primates, alongside clinical applications for deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS) in OCD, Parkinson’s, and depression. Dr. Rathi's work includes biophysical modeling of axon diameter estimation, functional connectivity analysis via fMRI, and development of real-time tools for precision targeting in neurosurgical interventions. His team secured significant NIH funding for harmonizing clinical diffusion MRI data. US Patent 10302727: Rapid Diffusion MRI Scanning NIH R01 Grant (4th percentile): MRI Harmonization Collaborator in $33M NIMH/FNIH Grant Key techniques developed by Dr. Rathi include real-time TMS targeting visualization and joint relaxation-diffusion MRI sequences for tissue characterization. These innovations are used in biomarker discovery and treatment monitoring.

Ashley Hugginsمشاهده پروفایل

استادیار

• Post-Traumatic Stress Disorder (PTSD)
• Anxiety Disorders
• Fear Conditioning
• +۳ مورد دیگر

Ashley Huggins serves as Assistant Professor and Clinical Director of the Cognition, Affect, and Traumatic Stress (CATS) Lab in the Department of Psychology at the University of Arizona, where she teaches advanced courses in abnormal psychology and cognitive neuroscience using cutting-edge neuroimaging methodologies. Education B.A., University of Southern California M.S., University of Wisconsin-Milwaukee Ph.D., University of Wisconsin-Milwaukee Clinical Internship in Neuropsychology, Charleston Consortium (Medical University of South Carolina and Charleston VA Medical Center) Research Focus Her work investigates the neurobiological mechanisms of PTSD and anxiety disorders through fear conditioning paradigms and structural/functional MRI , with particular emphasis on brain-based risk factors, resilience in older adults, and socioenvironmental impacts like community violence exposure. She explores critical intersections between cognitive and emotional processes in trauma-exposed populations, utilizing large-scale multi-site collaborations to identify neural biomarkers. Publication Trends Recent publications (2024-2025) demonstrate heavy involvement with the ENIGMA-PGC PTSD consortium, featuring mega-analyses of cerebellar volume reductions, resting-state connectivity disruptions, and differential effects of childhood trauma subtypes. Her work consistently applies data-driven computational approaches to neuroimaging data, revealing how neighborhood disadvantage, threat exposure, and social support modulate neural fear circuits and PTSD symptomatology across diverse populations. Laboratory Leadership As director of the CATS Lab, she oversees research examining neural substrates of traumatic stress through high-resolution fMRI (including 7T studies), fear generalization experiments, and longitudinal assessments of trauma recovery, with direct clinical applications for PTSD treatment development.

• Magnetic Resonance Imaging
• Deep Learning
• Compressed Sensing
• +۳ مورد دیگر

Mehmet Akcakaya is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on machine learning for medical imaging, especially MRI reconstruction using deep learning and compressed sensing. He leads NIH-funded projects and is accepting PhD students. His research interests include physics-informed deep learning , compressed sensing , and MRI reconstruction . He develops algorithms for high-resolution, accelerated MRI with applications in cardiac and brain imaging. Key challenges addressed are reference-free reconstruction and robustness in inverse problems. Recent work shows a trend toward self-supervised and unsupervised deep learning for MRI, with innovations in cycle-consistent learning and diffusion models for inverse problems. There is increasing emphasis on few-shot and zero-shot adaptation to handle limited training data. Dr. Akcakaya leads the NIH-funded project Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction and collaborates on the Center for Mesoscale Connectomics . He is accepting PhD students and mentors research in medical imaging and machine learning. His work is conducted at the University of Minnesota's Center for Magnetic Resonance Research, leveraging interdisciplinary collaborations in biomedical engineering and neuroscience.

Iman Hajirasoulihaمشاهده پروفایل

دانشیار

• Computational Biology
• Genomics
• Structural Variation Analysis
• +۶ مورد دیگر

Dr. Iman Hajirasouliha serves as Associate Professor of Systems and Computational Biomedicine at Weill Cornell Medical College since 2025. With a strong background in computational biology and genomics, they lead research at the intersection of bioinformatics, machine learning, and clinical medicine. Ph.D. in Computational Biology, Simon Fraser University (2012) M.Sc. in Computer Science, Simon Fraser University (2007) B.Sc. in Computer Engineering, Sharif University (2005) Their research focuses on three major areas: 1) Computational genomics for structural variation analysis and tumor phylogeny reconstruction, 2) Machine learning applications in reproductive medicine and embryo assessment, and 3) Metagenomic analysis of microbial communities and antibiotic resistance. They have developed innovative algorithms like Blackbird, Cue, and cloudrnaSPAdes for genomic data analysis. Recent publications highlight their work on spatial isoform sequencing in brain development, AI-based embryo ploidy prediction systems, and multi-omics analysis of disease states. Their research portfolio includes NIH-funded projects in computational biology and collaborations with the National Cancer Institute. Dr. Hajirasouliha's lab integrates computational methods with clinical applications, particularly in oncology and fertility medicine. They contribute to the development of the Tri-Institutional PhD Program in Computational Biology & Medicine and maintain active research collaborations across multiple institutions.

Basilis Zikopoulosمشاهده پروفایل

استادیار

• Connectomics
• Autism
• Systems Neuroscience
• +۵ مورد دیگر

Basilis Zikopoulos is an Assistant Professor at Boston University and Director of the Human Systems Neuroscience Laboratory ( http://sites.bu.edu/brainlab/ ). His research focuses on structural organization, molecular features, and connectivity of brain circuits in humans and animal models, particularly in the context of neurological disorders like autism, depression, and schizophrenia. Education: BS Biology, University of Crete (1996) MS Biology, University of Crete (1998) PhD Neuroscience, University of Crete (2004) Post-Doctoral Neuroscience, Boston University (2008) Using high-resolution microscopy, imaging, and computational modeling, his work explores the relationship between brain structure and function to identify mechanisms underlying neuropathology. His research integrates bench and clinical neuroscience to train future systems neuroscientists. His publications highlight cortical-thalamic interactions, autism-related pathologies, and computational frameworks for analyzing brain circuits. His lab employs advanced imaging and computational methods to address fundamental questions in neurodevelopmental and neurodegenerative diseases.