جست‌وجوی استاد و پژوهشگر

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

Delphine Périé-Curnierمشاهده پروفایل

استاد

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +۶ مورد دیگر

Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .

Yi-Zhuang Youمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Quantum Entanglement Dynamics
• Topological Phases
• +۴ مورد دیگر

Yi-Zhuang You is an Associate Professor in the Department of Physics at the University of California, San Diego (UCSD). He holds a Ph.D. from Tsinghua University (2013). His research focuses on theoretical investigations of correlated topological phases, quantum entanglement dynamics, and machine learning applications in many-body systems. Key areas include deconfined quantum criticality, symmetry-protected topological (SPT) phases, and the interplay between topology and quantum matter. His work bridges condensed matter physics and high-energy physics, exploring topics such as topological responses in gauge theories, entanglement holography, and quantum machine learning. Recent studies involve machine learning-driven approaches to quantum state preparation, symmetry discovery, and tomographic reconstruction of quantum systems. His contributions span theoretical frameworks for understanding topological transitions, fractionalization in lattice models, and the role of symmetry in quantum critical phenomena. Notable research highlights include the study of symmetric mass generation as a deconfined quantum criticality mechanism, the application of classical shadow tomography for efficient quantum state estimation, and the development of algorithms for self-similar dynamics modeling. His publications frequently intersect with experimental proposals for observing topological phases in materials like graphene and iridates. Dr. You’s affiliations include the UCSD Physics Department, with collaborations extending to institutions globally. His research is supported by grants focusing on quantum information, topological materials, and machine learning applications in physics. While no specific awards are listed, his work has been widely cited in high-impact journals across condensed matter and theoretical physics.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

• Numerical Analysis
• Numerical Methods for Partial Differential Equations
• Virtual Element Methods
• +۱۴ مورد دیگر

Stefano Scialo' is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab and serves as the contact person for the Bachelor's Degree Program in Mathematics for Engineering (L3). His academic journey began with a Master’s in Aerospace Engineering (2007), followed by a PhD in Mathematics for Engineering (2014), both from the same institution. After his PhD, he held postdoctoral and Assistant Professor positions at DISMA before being promoted to Associate Professor. Education: PhD in Mathematics for Engineering, Politecnico di Torino, 2014 Master in Aerospace Engineering, Politecnico di Torino, 2007 His research focuses on advanced numerical methods for partial differential equations, particularly in the context of complex multiscale and multiphysics systems. Key areas include the Virtual Element Method (VEM), domain decomposition techniques based on PDE-constrained optimization, and the simulation of flows in fractured porous media. He has made significant contributions to 3D-1D coupled problems, with applications in geosciences and biomedical modeling such as tumor-induced angiogenesis. His methodological work emphasizes robustness, scalability, and applicability to non-conforming and polygonal meshes, enabling high-performance computing solutions. The trend in his recent publications reveals a strong emphasis on developing and analyzing mixed virtual element methods, optimization-based coupling strategies, and their applications to engineering and biological systems. His work bridges theoretical numerical analysis with practical implementations in fluid dynamics and subsurface flow. Scientific Contributions: Principal Investigator of the FREYA project (2023–2026) on hybrid numerical approaches for fault reactivation. Coordinator of the INdAM-GNCS research project (2018–2019). Member of the research group "Numerical Analysis and Scientific Computing" at DISMA. Stefano Scialo' actively supervises doctoral students, including Matteo Trombini in the PhD program in Mathematical Sciences. He teaches a range of courses such as Advanced Scientific Programming in MATLAB, Numerical Methods and Scientific Computing, and specialized topics on Virtual Element Methods. He also contributes to curriculum development and academic governance through roles in doctoral colleges and degree program committees, including those for Mathematical, Mechanical, Aerospace, and Automotive Engineering. Laboratories and Research Groups: Member, Interdepartmental Center Ec-L - Energy Center Lab Research Group: Numerical Analysis and Scientific Computing (DISMA)

John D. E. Gabrieliمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Dyslexia
• Memory Systems
• +۷ مورد دیگر

John D. E. Gabrieli is a Professor at the Massachusetts Institute of Technology (MIT) in the Department of Brain and Cognitive Sciences, holding the Grover Hermann Professorship in Health Sciences and Technology and Cognitive Neuroscience. He serves as Director of the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research and leads the MIT Integrated Learning Initiative. His research spans cognitive neuroscience, focusing on memory, thought, and emotion, with clinical applications in dyslexia, autism, and psychiatric disorders. Director, Athinoula A. Martinos Imaging Center (2016–present) Grover Hermann Professor, MIT (2005–present) Faculty, Harvard Graduate School of Education (2006–present) Visiting Professor, Rush Medical College (1990–present) Gabrieli’s work combines brain imaging with behavioral studies to explore learning mechanisms in children, early intervention for reading difficulties, and neuroimaging applications in psychiatric diagnosis. Recent studies examine altered brain activity in dyslexia , neurodevelopmental patterns in autism , and predictive modeling for mental health . His 2024 dataset on adolescent anxiety/depression in the Human Connectome Project highlights longitudinal neuroimaging approaches. Scientific accolades include election to the American Academy of Arts & Sciences (2016), multiple teaching awards at MIT and Stanford, and the Alice H. Garside Lifetime Achievement Award (2017). He contributes to editorial boards of journals like Psychological Science and Biological Psychiatry . His lab emphasizes educational neuroscience , investigating how socioeconomic factors, white matter plasticity, and mindfulness interventions impact learning outcomes. Collaborations span Massachusetts General Hospital, McLean Hospital, and the Harvard Graduate School of Education, integrating clinical and educational applications of cognitive neuroscience.

• Forensic Psychiatry
• Expert Testimony
• Mental Competency
• +۷ مورد دیگر

Judith Gallen Edersheim, J.D., M.D. serves as an Assistant Professor of Psychiatry at Massachusetts General Hospital. With dual qualifications in law and medicine, her academic appointment is within the Department of Psychiatry at one of the nation's leading medical institutions. Her research interests span the critical intersection of psychiatry and law, with particular focus on forensic psychiatry, expert testimony, mental competency assessments, and the application of neuroimaging in legal contexts. Her work addresses complex issues including trauma and memory in asylum cases, elder abuse prevention, violence risk assessment, and the legal implications of psychiatric practice. Analysis of her publication record reveals a consistent trajectory examining how psychiatric knowledge applies to legal settings. Her recent work has increasingly focused on refugee mental health and trauma memory issues, while maintaining her longstanding interest in forensic applications of psychiatry and neuroimaging evidence. The publications demonstrate expertise in translating complex psychiatric concepts for legal contexts. Dr. Edersheim has collaborated extensively with prominent figures in forensic psychiatry including Rebecca Brendel, Bruce Price, and James Beck. Her work appears in leading journals such as the Journal of the American Academy of Psychiatry and the Law, Harvard Review of Psychiatry, and other specialized publications at the psychiatry-law interface. Her clinical and academic work appears to focus on providing psychiatric expertise for legal contexts, with particular attention to vulnerable populations including refugees, the elderly, and individuals involved in the criminal justice system. The conceptual analysis of her publications shows significant engagement with issues of expert testimony, forensic psychiatry, and trauma-related legal matters.

Dogyoon Songمشاهده پروفایل

استادیار

• Optimization
• Machine learning theory
• High-dimensional statistics
• +۱ مورد دیگر

Dogyoon Song is an Assistant Professor in the Department of Statistics at the University of California, Davis. His work bridges theoretical and applied domains in data science, with a focus on optimization, machine learning theory, and high-dimensional statistics. He also explores causal inference, contributing to foundational and algorithmic advancements in modern statistical learning. Research Interests: Optimization, Machine learning theory, High-dimensional statistics, Causal inference Publication Trends reveal expertise in high-dimensional inference, matrix estimation methods, and theoretical analysis of machine learning algorithms. His work spans temporal graph analysis, diffusion models, and robustness in learning systems, with applications to statistical modeling and optimization challenges.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

Zhengwu Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Brain Connectomics
• Deep Learning
• +۴ مورد دیگر

Zhengwu Zhang is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill. His research focuses on developing statistical and machine learning methods for analyzing high-dimensional neuroimaging data, particularly structural and functional brain connectomics. He leads the UNC Education Program of Intelligence and Connectomics (EPIC), an interdisciplinary initiative training students in brain network analysis. His work addresses challenges in large-scale neuroimaging datasets, including computational efficiency and reproducibility. Zhang completed his Ph.D. in Statistics at Florida State University under Anuj Srivastava. His funding includes NIH grants for CRCNS, structural connectome analysis, and personalized cognitive training. He serves as an Associate Editor for the Journal of the American Statistical Association (Reproducibility). Key contributions include tools like the Surface-Based Connectivity Integration (SBCI) GitHub repository for brain network analysis pipelines. His awards include the 2022 UNC Junior Faculty Development Award and the Oak Ridge Powe Award. Teaching roles include courses on data science, machine learning, and statistical consulting. His research spans brain network dynamics, genetic contributions to connectome structure, and applications of deep learning in neuroscience.

• Magnetic Resonance Imaging (MRI)
• Neuroimaging Biophysics
• Brain Metabolism Analysis
• +۶ مورد دیگر

Harald E. Möller is a Professor and Head of the Nuclear Magnetic Resonance Research and Development Unit at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. With a career spanning over four decades, he has held academic positions including Honorary Professor at the University of Leipzig and leadership roles in institutions like Duke University Medical Center and the University of Münster. His research focuses on advancing MRI methodologies, biophysical imaging principles, and their applications in neurology and neuroscience. Education: 1979-1985: Chemistry & Physics studies at Universities of Dortmund and Münster 1985: M.Sc. (Diploma) in Chemistry 1988: PhD in Physical Chemistry (summa cum laude) 2000: Habilitation in Physical Chemistry 2002: Habilitation in Biophysical Chemistry Research Interests: Development of novel MRI methods Quantitative tissue characterization Myelin sheath imaging Cerebral blood flow dynamics High-field MRI hardware

Nicholas Portمشاهده پروفایل

استاد

• Neuroscience
• Optometry
• Concussion Research
• +۷ مورد دیگر

Nicholas Port is a Professor at the Indiana University School of Optometry, where he has served since 2005. He holds a Ph.D. in Neuroscience from the University of Minnesota (1997) and completed postdoctoral training at the National Eye Institute's Laboratory for Sensorimotor Research. His research focuses on concussion/mTBI effects, athlete concussion epidemiology, neurobiology of brain injury, and human eye movement development. He is a key contributor to the NCAA-DoD CARE Consortium, advancing concussion assessment tools like VOMS and SCAT3. Education: Ph.D. in Neuroscience, University of Minnesota, 1997 Postdoctoral Training: National Eye Institute, 1997–2005 Research Interests: Concussion diagnosis and management in athletes Neurobiological mechanisms of TBI Developmental oculomotor systems Epidemiology of sports-related injuries His publications emphasize clinical and neuroimaging approaches to understanding concussion outcomes and athlete brain health. He teaches courses on ocular motility, biomedical systems, and neurobiology of concussion. His work bridges optometry, neuroscience, and sports medicine to address pressing health challenges in athletics. Advising & Grants: As a graduate faculty member with PhD committee leadership, Dr. Port mentors students in optometry and neuroscience. His research leverages interdisciplinary collaborations, such as the CARE Consortium’s large-scale athlete studies. Labs/Teams: Active in IU School of Optometry’s research programs, contributing to sensorimotor and vision science initiatives.

Peter J. Basserمشاهده پروفایل

استاد پژوهشی

• Quantitative Imaging
• Tissue Sciences
• Diffusion MRI
• +۷ مورد دیگر

Peter J. Basser is a leading research scientist at the National Institutes of Health (NIH), specifically within the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), where he heads the Section on Quantitative Imaging and Tissue Sciences (SQITS). His work bridges physics, engineering, and neuroscience to develop non-invasive MRI methods for probing tissue microstructure, particularly in the brain. His educational background is not explicitly mentioned, but his scientific achievements reflect deep training in biophysics and medical imaging. He earned his Ph.D. and has built a career at NIH as a principal inventor of key neuroimaging technologies. Basser's research focuses on quantitative imaging and tissue sciences , especially using diffusion MRI to study brain structure and function. He pioneered Diffusion Tensor MRI (DTI) , Streamline Tractography , and advanced methods like MAP MRI , CHARMED , and AxCaliber , enabling in vivo measurement of axon diameters and microstructural features previously accessible only through histology. His work aims to translate these tools into clinical use for diagnosing developmental disorders, trauma, and neurodegeneration. The 15 most recent articles reflect a consistent focus on developing novel MRI biomarkers, particularly through diffusion and relaxometry methods. They explore water exchange, restriction, glymphatic clearance, latency connectomes, and cortical microstructure, demonstrating a trajectory toward in vivo MRI histology and precision imaging for pediatric and neurological applications. Scientific Awards: National Academy of Engineering (NAE), Inducted 2020 National Academy of Inventors (NAI) Fellow, 2024 Eduard Rhein Technology Award, 2021 ISMRM Gold Medal, 2008 ISMRM Lauterbur Lecturer, 2020 American Society of Neuroradiology Honorary Member, 2019 Victor M. Haughton Award, 2017 ISMRM Fellow, 2010 AIMBE Fellow Best Paper Award, Frontiers in Physics, 2023 Basser leads a dynamic research group that mentors postdoctoral fellows and trainees, many of whom have received prestigious awards. His lab has secured significant grants from the NIH BRAIN Initiative, NICHD, USUHS, and the Bill & Melinda Gates Foundation. The SQITS lab develops open-source software tools like TORTOISE , dmritool , and HI-SPEED , which are widely used in the neuroimaging community. The lab collaborates with institutions such as Uniformed Services University and participates in major initiatives like the Human Placenta Project and the Human Connectome Project. Basser’s vision is to transform clinical MRI scanners into quantitative scientific instruments for precision medicine and large-scale brain mapping. Labs and Teams: Section on Quantitative Imaging and Tissue Sciences (SQITS), NICHD, NIH Neuropathology-Neuroradiology Integration Core (with USUHS) Advanced Translational Neuroimaging Research & Development Core Diffusion – Data Processing Center (DPC)

Martin Bendszusمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Neuroradiology
• Neuroimaging
• +۷ مورد دیگر

Professor Martin Bendszus serves as the Medical Director of the Department of Neuroradiology at Heidelberg University Hospital. He has held this position since 2007 and is a leading figure in advanced neuroimaging techniques. His academic career includes medical studies in Bonn, specialized training in Neuroradiology at the University of Würzburg (2003-2007), and a professorship in Neuroimaging at the University of Würzburg. Professor Bendszus's research focuses on innovative imaging methods, particularly in Magnetic Resonance Imaging (MRI). He pioneered Magnet Resonance Neurography for diagnosing peripheral nervous system disorders and made significant contributions to Dental-MRI as a radiation-free diagnostic tool for dental conditions. His expertise also extends to brain and spinal cord diagnostics, including aneurysms, strokes, and arteriovenous malformations. His work bridges clinical practice with advanced imaging research, resulting in numerous high-impact publications across neurology, radiology, and oncology. His publication record shows consistent high-impact contributions, with recent work spanning stroke intervention, neuropathic pain, brain tumor imaging, and advanced MRI techniques. The research trends demonstrate his leadership in translating imaging advances into clinical practice, particularly in time-sensitive neurological conditions where imaging guides critical treatment decisions. Kurt-Decker-Preis Röntgen-Preis Coolidge-Award Lucien-Appel-Award Hermann-Holthusen-Ring der Deutschen Röntgengesellschaft Professor Bendszus leads major multicenter clinical trials and collaborative guideline development efforts, including work with the Response Assessment in Neuro-Oncology (RANO) group and European Association for Neuro-Oncology (EANO). His research has secured significant funding for advancing neuroimaging techniques and their clinical applications. He collaborates extensively with neurologists, neurosurgeons, oncologists, and radiologists to improve diagnostic and therapeutic approaches for neurological conditions. His department serves as a reference center for complex neuroradiological cases and trains the next generation of neuroradiologists. Professor Bendszus maintains active leadership roles in professional societies and regularly contributes to shaping clinical guidelines in neuroimaging and stroke care.

• Alzheimer's Disease
• Huntington's Disease
• Parkinson's Disease
• +۵ مورد دیگر

Frank Longo is a Professor in the Department of Neurology and Neurological Sciences at Stanford University School of Medicine. He actively teaches across the neuroscience curriculum including Medical Scholars Research (NENS 370), Directed Reading courses (NEPR 299/NENS 299), Graduate Research (NENS 399/NEPR 399), and Early Clinical Experience (NENS 280), demonstrating his commitment to training the next generation of neuroscientists and clinicians. His research program centers on neurodegenerative diseases with particular emphasis on Alzheimer's, Huntington's, and Parkinson's diseases. Longo investigates the therapeutic potential of neurotrophin receptor modulation (especially p75NTR and TrkB) using small-molecule ligands like LM11A-31, exploring mechanisms spanning protein aggregation, synaptic dysfunction, neuroinflammation, and metabolic dysregulation. His work bridges molecular neuroscience with translational applications, utilizing advanced techniques including transcriptomics, neuroimaging, and biomarker analysis in cellular and animal models. Analysis of his 2024-2025 publications reveals consistent focus on targeting neurotrophin pathways to reverse disease phenotypes. Key themes include: 1) Development of small-molecule modulators for p75NTR/TrkB to rescue synaptic and cognitive deficits; 2) Biomarker discovery for early detection (e.g., plasma Aβ42/Aβ40 ratios); 3) Mechanistic studies on autophagy, white matter changes, and metabolic dysfunction; and 4) Leadership in collaborative initiatives like the TREAT-AD Center for target validation. His research demonstrates both breadth across neurodegenerative disorders and depth in neurotrophin signaling mechanisms. Through undergraduate and graduate research courses, Longo mentors students in neuroscience research despite no specific advisees being listed. His extensive publication record and involvement in multi-institutional consortia (including EU-US task forces) indicate significant research funding and national leadership in neurodegenerative disease research, though specific grant details aren't provided in the source material.