جست‌وجوی استاد و پژوهشگر

Yali Jiaمشاهده پروفایل

استاد

• Optical Coherence Tomography
• OCT angiography
• Retinal imaging
• +۱ مورد دیگر

Yali Jia, PhD is Professor of Ophthalmology and Biomedical Engineering and Jennie P. Weeks Professor of Ophthalmology at Oregon Health & Science University (OHSU). She serves as associate director of the Center for Ophthalmic Optics & Lasers and co-founded the International Ocular Circulation Society. Recognized as a world-leading expert in advanced ophthalmic imaging, Dr. Jia pioneered functional optical coherence tomography (OCT) and OCT angiography (OCTA) technologies with over 180 peer-reviewed publications, 13,000+ citations (h-index=48), and six co-edited books. Her research centers on Optical Coherence Tomography, OCT angiography, retinal imaging, and artificial intelligence applications for ophthalmic diagnostics. Dr. Jia's revolutionary SSADA technique enabled clinical deployment of OCTA across 1,000+ international centers. Current work integrates AI with multimodal imaging for automated pathology detection, widefield pediatric applications, and quantitative perfusion analysis. Recent breakthroughs include OCT oximetry (PNAS, 2020) and deep learning models for diabetic retinopathy classification (PRER, 2021). Analysis of her 15 most recent publications (2023-2025) reveals dominant trends in AI-enhanced OCT/OCTA systems, with 80% incorporating machine learning for vascular segmentation and pathology detection. Research spans pediatric applications (Retinopathy of Prematurity), neuro-ophthalmology (MS/glaucoma differentiation), and widefield imaging, demonstrating consistent focus on clinical translation of imaging biomarkers. Dr. Jia's scientific honors include: Special Scholar Award from Research to Prevent Blindness Carl Camras Translation Research Award from ARVO Fellow of the American Institute for Medical and Biological Engineering Senior Member of the National Academy of Inventors As principal investigator on federal, foundation, and industry-sponsored grants, she has secured funding for clinical translation of OCTA with over 10 licensed patents. Her NIH-funded projects focus on AI-driven diagnostic systems and handheld pediatric OCT devices, emphasizing real-world clinical implementation. Leading the imaging research arm of OHSU's Center for Ophthalmic Optics & Lasers, Dr. Jia directs a multidisciplinary team developing next-generation OCT systems. Her lab specializes in projection-resolved angiography, oximetry, and multimodal platforms for neuronal-vascular imaging, with active collaborations across neurology, pediatrics, and biomedical engineering departments.

Pascal Fuaمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +۶ مورد دیگر

Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.

Daniel Baumمشاهده پروفایل

استاد پژوهشی

• Scientific Visualization
• Computational Biology
• Image Analysis
• +۶ مورد دیگر

Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.

Curtis Bakerمشاهده پروفایل

استاد

• Visual Perception
• Neurophysiology
• Psychophysics
• +۱۲ مورد دیگر

Curtis Baker is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site, and a Professor in the Department of Ophthalmology and Visual Sciences at McGill University. His research focuses on understanding human visual perception , particularly low-level neural mechanisms that underpin everyday visual processing of figure-ground and local depth relationships through cues like contrast, texture, and motion . Institutional Affiliation: RI-MUHC, McGill University Academic Rank: Professor His lab employs human psychophysics , electrophysiology , optical imaging , and computational modeling to study how early visual processing detects and utilizes complex cues. Key research areas include receptive field dynamics , second-order boundary perception , and machine learning applications in visual neuroscience. Recent publications highlight his work on convolutional neural networks for receptive field estimation, Y-like neuronal responses in human vision, and texture regularity models using wavelet analysis. Collaborations span computational neuroscience , optical imaging , and depth perception mechanisms. Students and researchers in his lab include graduate students in Integrated Program in Neuroscience , Physiology , and Biomedical Engineering , alongside alumni contributing to neurophysiology and computational biology projects.

André Longtinمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Stochastic Dynamics
• Theoretical Biophysics
• +۶ مورد دیگر

André Longtin is a Full Professor in the Department of Physics at the University of Ottawa, affiliated with the Faculty of Science. He co-directs the Centre for Neural Dynamics & Artificial Intelligence and holds cross-appointments in Cellular and Molecular Medicine, Mathematics and Statistics. His research focuses on nonlinear dynamics, stochastic systems, and computational neuroscience, with applications to sensory processing, neural coding, and thermodynamic principles in biological systems. Education: B.Sc. (Honours Physics, Université de Montréal), M.Sc. (Physics, Université de Montréal), Ph.D. (Physics, McGill University, 1989). Postdoctoral training at Los Alamos National Laboratory. Research Interests: Theoretical biophysics, neural modeling, entropy production in biological systems, delayed dynamical systems, and interdisciplinary applications of nonlinear science. His group explores computations in hippocampus and zebrafish pallium, epilepsy mechanisms, neural coding in electrosensory systems, and autonomous stochastic rhythms. Recent Work: Articles highlight entropy dynamics in humans, myelin plasticity effects on neural networks, and reservoir computing with delayed loops. Awards include APS Fellowship (2003) and the NSERC Brockhouse Prize (2017). Editorial Roles: Biological Cybernetics, Frontiers in Computational Neuroscience, and others. Collaborations span systems neuroscience, clinical specialties, and engineering.

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

Zhuhao Wuمشاهده پروفایل

استادیار

• Neuroscience
• Neurovascular Coupling
• Neural Circuit Mapping
• +۴ مورد دیگر

Zhuhao Wu serves as Assistant Professor of Neuroscience at the Brain and Mind Research Institute, Weill Cornell Medical College since 2022. His research integrates neurovascular biology, neural circuit mapping, and neurodegenerative mechanisms to understand brain organization and disease processes. Education: Ph.D. in Neuroscience, The Johns Hopkins University School of Medicine (2011) B.S. in Biological Sciences, Tsinghua University, China (2003) Research Focus: Dr. Wu pioneers multi-scale investigations of neurovascular coupling , brain-wide circuit organization , and neurodegenerative pathways . His lab employs whole-brain imaging , single-cell transcriptomics , and genetic engineering in murine models to dissect mechanisms of stroke recovery, tau pathology, and developmental disorders. Current work emphasizes regional blood-brain barrier heterogeneity and axon degeneration pathways with therapeutic implications. Publication Trends: Recent work (2023-2025) reveals three convergent themes: (1) neurovascular dynamics in health/disease, (2) high-resolution brain atlasing techniques, and (3) molecular mechanisms of neurodegeneration. Publications in Cell , Nature , and Neuron demonstrate methodological innovation in circuit mapping and translational relevance to stroke, Alzheimer's, and autism spectrum disorders. Grant Portfolio: Principal Investigator Subaward: NINDS R01 Investigating Neurobiology of Early Cognitive Impairment (2024-2029) Principal Investigator Subaward: NINDS R01 Mechanisms of anosmia in COVID-19 (2023-2028) Principal Investigator Subaward: NINDS BRAIN CONNECTS Center for Large-scale Imaging (2023-2028) Principal Investigator Subaward: NINDS Global mapping of DDX3X mutation circuits (2023-2028) Principal Investigator Subaward: NIAID single-cell encephalitis pathogenesis (2023-2026) Dr. Wu leads a multidisciplinary team within the Brain and Mind Research Institute focused on developing HOLiS (whole-brain staining/clearing pipeline) and TrailMap for neural circuit analysis. His lab collaborates extensively on NIH BRAIN Initiative projects advancing large-scale connectome mapping.

• Neuroscience
• Electron Microscopy
• Synaptic Connectivity
• +۳ مورد دیگر

Angel Merchan Perez is a faculty member at the Universidad Politécnica de Madrid , affiliated with the College of Computer Science and the Computer Systems Architecture and Technology Department . He is a key member of the Center for Biomedical Technology (CTB) since 2011 and the Technologies for Health Sciences Research Group since 2018. His work bridges neuroscience and computational technologies, focusing on ultrastructural analysis of the brain. Doctoral Postdoc: Harvard Medical School (1992-1995) Current Projects: Cajal Blue Brain Project, Human Brain Project His research focuses on developing advanced 3D electron microscopy techniques (FIB-SEM) for synaptic reconstruction, enabling quantitative analysis of synapse distribution and density in rat, mouse, and human cerebral cortex . He also contributed to image-analysis software like Espina for automated synapse detection. Recent publications highlight his expertise in: 3D Synaptic Mapping in Hippocampal Neurons Neurodevelopmental Disorder Pathology (Schizophrenia, Autism) Thalamocortical Circuit Complexity Mitochondrial Distribution in Neuropil Software Tools for Electron Microscopy

• Neuromuscular Control
• Biomechanics
• Motor Control
• +۵ مورد دیگر

Professor Ian Loram is a leading academic in neuromuscular control and human movement science at Manchester Metropolitan University's Institute for Biomedical Research into Human Movement and Health (IRM). He holds roles as Academic Director of IRM and Academic Lead of the Biomechanics and Motor Control Research Group. His research focuses on sensorimotor control, postural stability, and applications of deep learning in medical imaging. Key contributions include studies on neuromuscular disorders, balance control mechanisms, and automated analysis of muscle function using ultrasound and neural networks. Education: PhD from University of Birmingham (2003) Awards: Leverhulme Early Career Fellowship (2004-2005) Grants: EPSRC Grants EP/F068514/1, EP/F069022/1, and EP/F06974X/1 ("Intermittent Control of Man and Machine") His research interests span neuromuscular control, biomechanics, postural dynamics, and clinical applications of imaging technologies . Recent work emphasizes automated analysis of muscle structure via ultrasound and deep learning, with clinical relevance to conditions like spinal muscular atrophy and cervical dystonia. Publications highlight interdisciplinary approaches, integrating control theory, neurophysiology, and computational methods to understand human movement. His lab develops tools for objective assessment of trunk control in children with cerebral palsy and explores the role of intermittent control in motor learning and balance. Labs/Teams: Biomechanics and Motor Control Research Group Healthcare Science Research Institute

• Molluscan Evolution
• Evolutionary Biology
• Developmental Biology
• +۳ مورد دیگر

Tim Wollesen is an Associate Professor in the Department of Evolutionary Biology at the Faculty of Life Sciences. His research focuses on molluscan evolution, neurogenesis, and developmental genetics, particularly in cephalopods, scaphopods, and chaetognaths. He has led projects funded by the Schrödinger Grant and collaborates internationally on topics like Hox gene expression and single-cell transcriptomics. Research interests include evolutionary developmental biology (evo-devo), comparative genomics, and the molecular mechanisms underlying body plan diversification. Recent studies explore the genetic basis of shell formation, nervous system evolution, and ancestral cell type conservation in spiralians. Notable contributions include analyzing developmental gene expression in squid eyes, sequencing chaetognath transcriptomes, and elucidating Hox gene roles in molluscan body plans. His work bridges molecular biology with evolutionary questions, contributing to UN Sustainable Development Goals through biodiversity research. Received Schrödinger Grant (2018) to support evolutionary research Active in international conferences, presenting on bioadhesives and molluscan cell types Lead researcher on projects like 'Elucidating molluscan evolution by single cell sequencing' (2021–ongoing) His lab focuses on integrative approaches combining genomics, microscopy, and developmental biology to decode evolutionary innovations in invertebrate lineages.

Dawen Caiمشاهده پروفایل

دانشیار

• Neuroscience
• Developmental Biology
• Molecular Biology
• +۳ مورد دیگر

Dawen Cai, Ph.D., is an Associate Professor at the University of Michigan Medical School in the Department of Cell and Developmental Biology , with a secondary affiliation in the Biophysics Department under the College of Literature, Science, and the Arts (LS&A). He is also affiliated with the Neuroscience Graduate Program at the Medical School. His research focuses on integrating computational and experimental approaches to study neuronal subtype determination using scRNA-seq and in situ analysis. His research explores the intersection of RNA biology, neuroscience, and bioinformatics. He develops tools for multispectral imaging and lineage tracing to decode neural development and connectivity in Drosophila and mammalian models. His work combines single-cell transcriptomics with advanced microscopy to identify marker genes and model neuronal architecture. The articles reflect a strong interdisciplinary focus on neuroscience and biomedical imaging. Recent publications highlight innovations in 3D imaging technologies, image compression algorithms, and machine learning applications for medical image segmentation. These works emphasize scalable solutions for high-resolution data analysis, advancing tools for neurophysiology, and leveraging RNA sequencing to map neural development. No scientific awards were explicitly mentioned in the text. Dawen Cai actively recruits PhD students and postdoctoral fellows for the Cai Lab, prioritizing candidates with wet-lab skills, bioinformatics expertise, and experience in quantitative image processing. His lab emphasizes training in interdisciplinary research, paper/grant writing, and critical thinking.

Marjan Firouzniaمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.

Sujith Vijayanمشاهده پروفایل

دانشیار

• Computational modeling
• Brain oscillatory dynamics in health and disease
• Development of stimulation and pharmacological therapies for brain disorders
• +۷ مورد دیگر

Sujith Vijayan is an Associate Professor at the School of Neuroscience , part of the Virginia Tech College of Science . His research focuses on neural dynamics during sleep and active behavior , leveraging computational modeling , signal processing , and invasive/non-invasive recording to explore memory consolidation, emotional regulation, and therapeutic applications for neurological disorders. Research Interests include: Sleep-mediated memory processing Brain-computer interface (BMI) learning Pathological brain rhythms in PTSD and Parkinson’s Neural stimulation therapy development REM sleep emotional memory mechanisms His 15 most recent publications reveal a trajectory from anesthetic-induced neural oscillations (2013), through schizophrenia network analysis (2015), to targeted memory reactivation during sleep (2017). Key scientific contributions include the 2022 NSF CAREER award for BCI learning enhancement via sleep manipulation . Lab members include graduate students in Biomedical Engineering, TBMH, and Neuroscience programs, with alumni now at institutions like Barrow Neurological Institute and University of Arizona’s Sleep Lab.

Yuki Ogawaمشاهده پروفایل

استادیار

• Neurobiology
• Neuronal Structure
• Neural Connectivity
• +۶ مورد دیگر

Yuki Ogawa is an Assistant Professor in the Department of Biological Sciences at the University of South Carolina , affiliated with the College of Arts and Sciences . Her research focuses on the molecular mechanisms governing neuronal structure and connectivity, particularly the role of the axon initial segment (AIS) in synaptic formation and neuropsychiatric disorders like autism spectrum disorders . Her lab employs proteomics , adeno-associated viral vectors , and CRISPR genome editing to study AIS development and synapse maintenance. They also prioritize biochemical tool development to address unresolved neuroscience questions. Key collaborators include Matthew Rasband and Elisa V. de Freitas . Recent publications explore K2P K+ channel clustering , TRIM46 function , and localized gene transfer methods . The lab's work intersects neurobiology , cellular dynamics , and neuroengineered therapies , with implications for neuropsychiatric disease mechanisms. For inquiries, contact her at yuki.ogawa@sc.edu or visit the Ogawa Lab Website .

• Systems biology
• Artificial Intelligence
• Neural Networks
• +۷ مورد دیگر

Professor Sandhya Samarasinghe is a leading researcher at Lincoln University's School of Landscape Architecture where she directs the Complex Systems, Big Data and Informatics Initiative (CSBII). With expertise spanning computational biology, AI, and complex systems modeling, she bridges theoretical frameworks with practical applications across biological, agricultural, and environmental domains. PhD, Virginia Tech, Blacksburg, United States MS, Virginia Tech, Blacksburg, United States MSc, International University of Moscow, Moscow, Russia Professor Samarasinghe's research centers on developing advanced computational methodologies for modeling complex systems. Her work explores how soft computing techniques—including neural networks, fuzzy systems, and machine learning—can unravel complexity in biological networks and environmental systems. She advances the view of living organisms as evolved information systems and develops conceptual frameworks for understanding biological complexity from molecular to organismal levels. Her research has significant applications in medical diagnostics, sustainable agriculture, and environmental management. Analysis of her recent publications reveals a strong emphasis on neural network applications across diverse domains, with particular focus on biological systems modeling, Alzheimer's disease research, agricultural technology, and environmental sustainability. Her work consistently integrates theoretical computational approaches with practical problem-solving. Fellow of Modelling and Simulation Society of Australia and New Zealand Senior Member of IEEE Visiting Fellow at Oxford University Visiting Fellow at Princeton University Visiting Fellow at Stanford University Visiting Fellow at CSIRO Professor Samarasinghe has supervised over 25 postgraduate students across diverse research areas including mastitis detection in dairy cattle, neural modeling of cell cycles, autonomous self-repair systems, and computational approaches to vaccine development. She leads the Complex Systems, Big Data and Informatics Initiative which develops cutting-edge computing solutions for complex biological and environmental challenges.