جست‌وجوی استاد و پژوهشگر

Polina Gollandمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Medical Vision
• AI & Machine Learning
• +۸ مورد دیگر

Polina Golland is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and a Principal Investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on developing novel techniques for biomedical image analysis and understanding, particularly in medical vision, AI/ML, and health care applications. She leads the Medical Vision Group and collaborates with the Vision Group at CSAIL. Her work emphasizes statistical modeling of medical images, shape modeling, and predictive analytics for biological processes. Current projects include fetal MRI analysis, cardiac MRI segmentation, and quantitative assessment of pulmonary edema in chest X-rays. She has secured grants from NIH, MIT-IBM Watson AI Lab, and other institutions to support her research. Dr. Golland teaches courses on inference, probability, and probabilistic systems. She advises graduate students in MIT's EECS program and has mentored numerous postdocs and researchers. Her lab focuses on translating advanced imaging techniques into clinical workflows, with applications in neuroimaging, fetal health monitoring, and cardiovascular disease analysis. Notable collaborations include work with Harvard Medical School affiliates, Brigham and Women's Hospital, and the MIT Jameel Clinic. Her research aims to bridge computational methods with clinical needs, improving diagnostic tools and treatment planning through machine learning and medical imaging innovation.

Robert Rohlingمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Medical Information Systems
• +۳ مورد دیگر

Robert Rohling is a Professor at the University of British Columbia's Faculty of Applied Science, affiliated with the Department of Mechanical Engineering and holding a joint appointment with the Department of Electrical and Computer Engineering. As Director of the Institute of Computing, Information and Cognitive Systems (ICICS), his research focuses on biomedical engineering, medical imaging, robotics, and computational methods. B.A.Sc. (UBC) M.Eng. (McGill) Ph.D. (Cambridge) Rohling's work spans three primary research areas: medical imaging (3D ultrasound, spatial compounding, elasticity reconstruction), medical information systems (radiologist navigation tools for large image datasets), and robotic calibration for surgical applications. His multidisciplinary approach integrates mechanical and electrical engineering principles with clinical needs. Rohling's publications (2020-2022) reveal trends in advanced ultrasound techniques (e.g., shear wave vibro-elastography), AI-driven image processing (cycleGAN translation), and computational optimization for diagnostic accuracy. Keywords across his work include Medical Imaging, Biomedical Engineering, Robotics, and Computational Modeling. As director of the Robotics and Control Laboratory , Rohling leads interdisciplinary collaborations with industry and clinical partners to address practical challenges in medical diagnostics and surgical robotics. His research emphasizes translating engineering innovations into clinical practice.

Daniel Duttonمشاهده پروفایل

دانشیار

• Social Epidemiology
• Public Health Policy
• Health Economics
• +۳ مورد دیگر

Dr. Daniel Dutton is an Associate Professor in the Department of Community Health and Epidemiology at Dalhousie University's Faculty of Medicine, based at the Dalhousie Medicine New Brunswick campus in Saint John. His research focuses on how social environments influence health outcomes and the role of government policies in mitigating these impacts, particularly in areas like poverty, homelessness, and health economics. He holds adjunct roles at the University of New Brunswick (Sociology) and is the Scientific Director of APPTA, a hub bridging aging research and policy. He teaches courses in epidemiology, population health, and statistics. Education: PhD in Community Health Sciences (University of Calgary) MA in Economics (University of Calgary) BA Honours in Economics (Queen’s University) Research Interests: Dr. Dutton examines population-level health determinants, government policy effectiveness, and socioeconomic disparities. His work employs large datasets and econometric models to analyze topics such as homelessness incidence, healthcare cost savings through preventive measures, and the distributional impacts of public spending. Key Contributions: Scientific Director of the APPTA aging and technology hub. Co-founder of the IMPART research collaborative. Author of influential studies on Housing First programs, opioid prescribing patterns, and the fiscal benefits of guaranteed annual income policies. Advising & Grants: Supervises multiple MSc students and has collaborated on grants analyzing healthcare utilization in New Brunswick and Alberta. His work often intersects with policy-making, emphasizing actionable insights from quantitative research. Labs & Teams: Leads the Dutton Research Group, which uses mascot Odin the Saint Bernard to foster team cohesion. Active in interdisciplinary collaborations across public health, sociology, and economics.

Massimo Mischiمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

Jeremy Dahlمشاهده پروفایل

استاد

• Medical Ultrasound Imaging
• Ultrasonic Beamforming
• Image Reconstruction
• +۷ مورد دیگر

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.

• Medical Image Analysis
• Machine Learning in Medical Imaging
• Neuroimaging
• +۵ مورد دیگر

Lawrence Staib is Professor of Radiology and Biomedical Imaging, Biomedical Engineering, and Electrical Engineering at Yale University. He serves as Director of Undergraduate Studies in Biomedical Engineering and is a member of Yale's Bioimaging Sciences division, Image Processing & Analysis Group, Yale Biomedical Imaging Institute, and Yale-BI Biomedical Data Science Fellowship program. Dr. Staib earned his A.B. in Physics from Cornell University (1982), followed by a Ph.D. in Engineering and Applied Science from Yale University (1990), and completed a postdoctoral fellowship at Yale School of Medicine (1991). His research focuses on developing advanced medical image analysis methods using machine learning and model-based approaches. Key research areas include neuroimaging applications for autism spectrum disorder classification, cardiac imaging analysis for strain and motion assessment, prostate cancer diagnosis and risk mapping, and innovative techniques for medical image segmentation with limited labeled data. Dr. Staib's work emphasizes uncertainty estimation in deep learning models, multi-modal image registration, and domain adaptation techniques to improve clinical decision support systems. His recent publications demonstrate a strong trend toward developing interpretable AI models for clinical applications, with particular emphasis on fMRI analysis for neurological conditions, cardiac motion analysis, and prostate cancer diagnosis. His work frequently addresses the challenge of limited labeled data in medical imaging through innovative self-supervised, semi-supervised, and few-shot learning approaches. Fellow of the American Institute for Medical and Biological Engineering (AIMBE) (2015) Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research (2017) MICCAI Fellow (2022) Medical Image Analysis Second Best MICCAI Paper Award (2005) ASNR Cum Laude Scientific Exhibit Award (2003) Dr. Staib serves on the editorial board of Medical Image Analysis and as Associate Editor of IEEE Transactions on Biomedical Engineering. His research is supported by NIH grants including the Autism Center of Excellence program. He leads the Image Processing & Analysis Group within Yale's Bioimaging Sciences division, collaborating extensively with James Duncan, John Onofrey, Xenophon Papademetris, and other Yale researchers on applications spanning neuroimaging, cardiology, and oncology. Current projects focus on developing robust AI models for clinical decision support with emphasis on uncertainty quantification and interpretability.

Anthony A Gattiمشاهده پروفایل

پژوهشگر ارشد

• Biomechanics
• Medical Imaging
• Machine Learning
• +۷ مورد دیگر

Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.

Xenophon Papademetrisمشاهده پروفایل

استاد

• Medical Image Analysis
• Machine Learning
• Biomedical Software Development
• +۷ مورد دیگر

Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +۶ مورد دیگر

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

Ryan Keith Shostedمشاهده پروفایل

استاد

• Phonetics
• Phonology
• Historical Linguistics
• +۶ مورد دیگر

Prof. Ryan Keith Shosted is a full-time tenured Professor at the University of Illinois at Urbana-Champaign , affiliated with the Department of Linguistics , Spanish and Portuguese , American Indian Studies Program , Beckman Institute , Lemann Center for Brazilian Studies , Center for Latin American and Caribbean Studies , and Center for African Studies . He serves as Director of the Program in Translation and Interpreting Studies and leads the Chin-Woo Kim Phonetics Laboratory . Education: Ph.D. , Linguistics, University of California, Berkeley (2006) M.A. , Linguistics, University of California, Berkeley (2003) B.A. , Linguistics, Brigham Young University (2000) Shosted's research focuses on the intersection of phonetics , phonology , and historical linguistics . He pioneered the application of ultrafast dynamic MRI to study the vocal tract's physiological-acoustic mapping in diverse languages, including Hittite cuneiform , Deseret Alphabet , and endangered languages like Q'anjob'al. His work spans speech production modeling , nasalization mechanisms , and cross-linguistic articulatory analysis . The 15 most recent publications demonstrate his leadership in dynamic speech imaging , phonetic-aerodynamic modeling , and historical sound change analysis . Key trends include advanced MRI techniques for speech study, phonetic universals , and historical writing systems as tools for linguistic reconstruction. Scientific Awards: Campus Award for Excellence in Undergraduate Teaching (2021) Dean's Award for Excellence in Undergraduate Teaching (2021) Arnold O. Beckman Award (2009, 2010) Jacob K. Javits Fellowship (2001-2005) Shosted's grant portfolio includes NSF funding for nasalization research (BCS-1651197, BCS-1121780) and NIH collaboration (1R01DE027989-01A1) on cleft palate speech. He has directed 12 graduate students and taught courses ranging from Hittite language to quantitative phonetic methods . The Chin-Woo Kim Phonetics Laboratory , under his directorship since 2007, expanded in 2010 to include articulatory phonetics facilities with EPG, ultrasound, and MRI analysis capabilities. He continues to lead Beckman Institute collaborations in speech imaging technology.

James Lacefieldمشاهده پروفایل

استاد

• Biomedical Ultrasound Imaging
• Vascular Imaging
• Cancer Research Applications
• +۵ مورد دیگر

James Lacefield is a Professor in both the Department of Electrical and Computer Engineering and the Department of Medical Biophysics at Western University. He serves as Director of the School of Biomedical Engineering and maintains his research laboratory in the Amit Chakma Engineering Building. His academic appointments span multiple disciplines, reflecting the interdisciplinary nature of his work in biomedical ultrasound imaging. Dr. Lacefield earned his Ph.D. and B.S.E. in Biomedical Engineering from Duke University. His educational background established the foundation for his current research program that bridges engineering principles with medical applications. His research focuses on the physical acoustics and signal processing aspects of ultrasound imaging, with particular emphasis on quantitative vascular imaging applications. Dr. Lacefield's laboratory develops novel methods for color Doppler, power Doppler, and contrast-enhanced ultrasound imaging, with primary applications in cancer research. Current projects include optimization of high-resolution ultrasound systems for tumor vascular characterization and development of methods to quantify spatial blood flow distribution in tumors. Analysis of his recent publications reveals a strong focus on quantitative ultrasound techniques for cancer applications, with particular attention to tumor perfusion assessment using contrast-enhanced ultrasound. His work demonstrates increasing sophistication in speckle analysis methods to improve the reliability of perfusion measurements in preclinical tumor models. Associate Editor, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control Member, Council of Chairs of Bioengineering and Biomedical Engineering Member, College of Reviewers, Canadian Institutes of Health Research Dr. Lacefield maintains active collaborations with multiple research groups including the Imaging Research Laboratories at Robarts Research Institute. His professional activities include editorial work for major ultrasound journals and participation in national review panels for biomedical research funding.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Neal Bangerter is a Visiting Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He specializes in medical imaging (MRI), artificial intelligence, machine learning, and signal processing. Dr. Bangerter holds adjunct appointments at INSEAD, the University of Utah, and Brigham Young University. His research focuses on ultra-high field MRI, AI applications in healthcare, and data-driven bioscience technologies. He leads the London Collaborative Ultra-High Field Scanner (LOCUS) project and advises companies on AI and innovation strategies. Education: B.S. in Physics (UC Berkeley), M.S. and Ph.D. in Electrical Engineering (Stanford University). Career highlights include roles at McKinsey & Company, Microsoft, and Reactrix, as well as founding BYU's Medical Imaging Research Center. He has pioneered cross-faculty initiatives like the Crocker Innovation Fellowship Program. Research interests include novel MRI pulse sequences, AI in medical imaging, and large-scale health data analysis. His work spans collaborations with Stanford, Oxford, Cambridge, and Siemens Healthcare. He teaches executive education at INSEAD, focusing on bridging technical concepts with business strategies. Key awards include the David Evans Chair at Brigham Young University. His contributions to the UK Biobank Neuroimaging study and development of MRI techniques like RAFO-4 highlight his impact on advancing imaging technologies and AI applications in healthcare.

• Veterinary Surgery
• Clinical Sciences
• Veterinary Anesthesiology
• +۳ مورد دیگر

Dr. Candemir ÖZCAN is an Assistant Professor at the Department of Clinical Sciences, Faculty of Veterinary Medicine, Kastamonu University. His academic career spans veterinary surgery, clinical diagnostics, and comparative medicine. Adnan Menderes University (BSc, Veterinary Medicine, 2013) Mehmet Akif Ersoy University (PhD, Veterinary Surgery, 2018) Burdur Mehmet Akif Ersoy University (PhD, Veterinary Surgery, 2022) Research focuses on veterinary ophthalmology, dental thermography, and hormonal impacts on clinical metrics. Key methodologies include thermal imaging, intraocular pressure analysis, and estradiol biomarkers. His 15 most recent publications highlight diagnostic imaging, anesthesiology, and periodontal disease dynamics. Scientific contributions include: 2022 Oral Presentation First Prize from Güven Plus Group A.Ş. 5 funded projects (2018-2025) addressing hoof diseases, neuro-ocular interactions, and imaging techniques Collaborations with researchers like Kürşad Yiğitarslan (13 co-publications) and Elif Doğan (5 co-publications)

Jalal Al-Tamimiمشاهده پروفایل

دانشیار

• Phonetics
• Phonology
• Laboratory Phonology
• +۷ مورد دیگر

Jalal Al-Tamimi is an Associate Professor (Maître de conférences) in experimental phonetics and phonology at Université Paris Cité, affiliated with the Laboratoire de Linguistique Formelle (LLF). He holds an HDR (Habilitation à Diriger des Recherches) in Phonetics and Phonology and has extensive experience in teaching and research across institutions including Newcastle University, UK. His research focuses on the interface between phonetics and phonology, particularly the role of acoustic and articulatory correlates in speech production, perception, and acquisition. He specializes in Arabic dialects, using advanced quantitative methods like generalized additive mixed models and machine learning techniques. Al-Tamimi’s academic journey includes a PhD from Lyon 2 University (2007) and postdoctoral roles in the UK before joining Paris. He currently directs the Master in Language Sciences and co-leads research strands on phonetic complexity and language variation. His administrative roles include membership in the AFCP and coordination of experimental linguistics at LLF. Research interests span laboratory phonology, articulatory-acoustic mapping, and the application of automated methods in clinical diagnosis. His work explores epilarynx dynamics in Arabic gutturals and forced-alignment systems for dialectal Arabic. Awards include the Peter Ladefoged Prize (2018) and Leverhulme Fellowships. He supervises numerous PhD and MA students across computational linguistics and phonetics. Publications highlight contributions to vowel dynamics, speech processing in Alzheimer’s, and cross-linguistic phonological studies. His lab collaborations involve the MAUS team and Sorbonne’s SCAI, focusing on speech technology and pathological speech analysis.

• Speech Science
• Phonetics
• Language Revitalization
• +۳ مورد دیگر

Dr Chris Carignan serves as Programme Director for the Language Science MSc at University College London's Division of Psychology and Language Sciences within the Faculty of Brain Sciences. His research focuses on the intricate mechanisms of human speech production, particularly through cross-linguistic phonetic studies and articulatory dynamics using advanced imaging technologies. PhD in Speech Science Specializes in the intersection of language heritage and phonetic research Develops innovative methodologies for speech data collection and analysis His work spans multiple areas including nasal coarticulation , real-time MRI of vocal tract movements , and cross-linguistic phonetic analysis . He has contributed significantly to understanding how speakers produce complex speech sounds across different languages through comparative studies. Dr Carignan's research has led to the development of open-source tools for speech production analysis and pioneered new approaches for ultrasound articulography . His work on vowel nasalization and sibilant contrasts has been particularly influential in speech science. As Programme Director, he oversees a comprehensive curriculum that provides students with methodological training , statistical expertise , and hands-on research experience while allowing specialization through optional modules. His teaching philosophy emphasizes the importance of curiosity-driven research and interdisciplinary approaches in advancing language sciences.