جست‌وجوی استاد و پژوهشگر

Peter O'Tooleمشاهده پروفایل

استاد

• Fluorescence Imaging
• Flow Cytometry
• Microscopy Technology Development
• +۳ مورد دیگر

Dr. Peter O'Toole is Head of Imaging and Cytometry and a Professor in the Department of Biology at the University of York. He leads the Imaging and Cytometry Laboratory, a top European facility equipped with state-of-the-art equipment from collaborators like Carl Zeiss and Beckman Coulter. His research focuses on microscopy technology development, with expertise in fluorescence imaging, flow cytometry, and super-resolution techniques. He holds grants from Wellcome Trust, BBSRC, and others, including a £954k Wellcome Trust grant for hyperspectral imaging technology. O'Toole is also Vice President of the Royal Microscopical Society and chairs major microscopy conferences. His educational background includes a PhD in Cell Biophysics from the University of Essex (1998) and a BSc from the same institution (1994). Research interests include developing imaging tools for biomedical applications, immunometabolism, and live-cell analysis. He collaborates internationally on projects like PHENOSPOT and the UK Coronavirus Immunology Consortium. His lab supports cutting-edge projects such as spatial omics and therapeutic vaccine development for leishmaniasis. O'Toole organizes microscopy courses and has pioneered facility meetings for the microscopy community. Grants total over £20M, with recent focuses on deep learning in imaging, immune system metabolism, and hyphae characterization. Key roles include Director of the Bioscience Technology Facility and membership in grant panels for BBSRC, MRC, and Wellcome Trust. His team includes Senior Technicians and PhD students like Laura Wiggins, advancing imaging technologies and collaborative research.

Andrew Shoffstallمشاهده پروفایل

دانشیار

• Biomaterials
• Neural Engineering
• Neuroprosthetics
• +۳ مورد دیگر

Andrew Shoffstall is the Nord Distinguished Associate Professor in the Department of Biomedical Engineering at Case Western Reserve University’s Case School of Engineering, and serves as Associate Chair of the Graduate Education Committee. His research bridges biomaterials and neural engineering, focusing on improving neural interfaces through minimally invasive techniques, biomaterial innovation, and clinical translation. He emphasizes overcoming challenges like neuroinflammation, trauma during implantation, and commercial viability in neural device design. His work integrates computational modeling, material science, and clinical constraints to advance technologies such as injectable electrodes and anti-inflammatory nanoparticles. Key contributions include studies on vagus nerve anatomy, spinal cord stimulation, and reducing implant-associated inflammation. He leads the Shoff Lab ( www.shofflab.com ), fostering interdisciplinary research in neural engineering and biomaterials. Recent publications highlight advancements in electrode design, nanoparticle therapies, and imaging techniques for nerve anatomy. His research has implications for treating neurological disorders and improving long-term device performance in clinical settings.

Paul Parkمشاهده پروفایل

دانشیار

• Retinal Degeneration
• GPCR Biology
• Photoreceptor Cell Biology
• +۳ مورد دیگر

Paul Park, PhD, is an Associate Professor in the Department of Ophthalmology and Visual Sciences at Case Western Reserve University's School of Medicine. His primary appointment is in Ophthalmology, with secondary appointments in Pharmacology and Biochemistry. His research focuses on photoreceptor cell biology, rhodopsin structure-function relationships, and molecular mechanisms underlying retinal degeneration. He investigates G protein-coupled receptors (GPCRs), particularly rhodopsin mutations linked to inherited vision disorders such as retinitis pigmentosa. Key Research Areas: Rhodopsin dysfunction, membrane protein organization, protein aggregation, and retinal disease mechanisms. Techniques: Atomic force microscopy (AFM), Förster resonance energy transfer (FRET), cryoelectron tomography, and biochemical/biophysical assays. His lab explores how rhodopsin mutations cause structural instability and aggregation, leading to photoreceptor cell death. Recent work includes studying nanoscale organization of rhodopsin in rod outer segment membranes and the role of palmitoylation in receptor stability. Publications highlight structural analysis of rhodopsin mutants, oligomerization dynamics, and disease models. No scientific awards explicitly listed in provided texts. Labs/Teams: The Park Lab at Case Western Reserve University focuses on translational research bridging molecular mechanisms to therapeutic strategies for retinal diseases.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +۳ مورد دیگر

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Robert Azencottمشاهده پروفایل

استاد

• Genomics
• Proteomics
• Medical Imaging
• +۹ مورد دیگر

Robert Azencott is a Professor of Mathematics at the University of Houston and holds the title of Emeritus Professor at École Normale Supérieure in France. He specializes in interdisciplinary research at the intersection of mathematics, biosciences, and imaging. His work spans stochastic processes, data mining, and medical imaging applications such as 3D-echocardiography analysis and deformable shape matching. Azencott has contributed to probabilistic methods for bacterial genetic evolution models and financial market microstructure analysis. His research also addresses texture classification, kernel-based learning, and stochastic differential equations (SDEs) in finance and biology. Research Interests Genomics & Proteomics : Sparse modeling of gene interactions, microRNA impact on cancer survival, proteomic mass spectra analysis. Bacterial Evolution : Stochastic models for genetic evolution with periodic selection and large deviations theory. Medical Imaging : 3D-echocardiography strain analysis, deformable shape dynamics via diffeomorphic matching, and ROI reconstruction in X-ray tomography. Probability & Statistics : SDE parameter estimation, kernel-based clustering, and applications to algorithmic trading and option pricing. Labs & Collaborations Azencott collaborates on projects such as digital stains for live-cell microscopy and intra-cardiac echography analysis to assess myocardium deformations. His work intersects with teams in cardiology, computational biology, and financial engineering.

Alejandro Luis Callaraمشاهده پروفایل

پژوهشگر

• Biomedical Signal Processing
• Neuroimaging
• Brain Connectivity
• +۶ مورد دیگر

Alejandro Luis Callara serves as a postdoctoral researcher at the University of Pisa's Research Center "E. Piaggio," specializing in biomedical signal and image processing methodologies for neuroscience applications. His work bridges engineering and cognitive science through advanced computational approaches to physiological data analysis. His academic foundation includes: Bachelor’s Degree in Biomedical Engineering (2012), University of Pisa Master’s Degree in Biomedical Engineering (2015), University of Pisa PhD in Information Engineering (2019), University of Pisa Callara's research centers on developing analytical pipelines for EEG and fMRI data to map brain connectivity networks, with particular expertise in autonomic nervous system interactions during emotional processing. He actively contributes to the Brain matters team's development of neuronal segmentation tools for confocal microscopy data, extending his methodological focus to multi-scale tissue imaging. His technical proficiency spans directed coherence analysis, dynamic causal modeling, and thermal response quantification. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) autonomic physiology investigations examining parasympathetic-sympathetic coupling during emotional tasks using EDA and HRV metrics; (2) innovative neuroimaging techniques for brainstem fMRI analysis and sparse connectivity mapping; and (3) multimodal integration studies exploring olfactory-visual-auditory interactions in affective disorders through VR/AR paradigms. Methodologically, his work consistently employs advanced signal processing frameworks like PCA-based partial correlation and ThermICA for multivariate analysis. Within the Research Center "E. Piaggio," Callara collaborates across bioengineering and robotics initiatives, particularly through the Brain matters team where he applies computational neuroscience to neuronal morphology analysis. His current projects focus on refining contactless stress classification systems using thermal imaging and developing immersive VR scenarios for anxiety disorder research, demonstrating strong translational potential for clinical applications.

Alejandro Parada-Mayorgaمشاهده پروفایل

استادیار

• Algebraic Neural Networks
• Signal Processing
• Machine Learning
• +۲ مورد دیگر

Alejandro Parada-Mayorga is an Assistant Professor in the Department of Electrical Engineering at the University of Colorado Denver, specializing in mathematical foundations of deep learning and signal processing. His research develops theoretical frameworks for neural networks using algebraic structures and noncommutative algebras. Parada-Mayorga's work on Algebraic Neural Networks provides universal stability analysis applicable across convolutional architectures, graph neural networks, and group neural networks. His current Leverhulme Early Career Fellowship project, co-led with Professor David Galbreath, investigates human-machine teaming and future military operations. Parada-Mayorga's research has been published in premier IEEE transactions and conferences. He received his PhD in Electrical Engineering from the University of Delaware where he was awarded the Dissertation Fellowship and Signal Processing and Communications Graduate Faculty Award.

James Williamsonمشاهده پروفایل

استاد

• RNA Structure
• Ribosome Biogenesis
• Structural Biology
• +۳ مورد دیگر

James Williamson, PhD, is a Professor and Chair in the Department of Integrative Structural and Computational Biology at Scripps Research. He holds joint appointments in Chemistry and the Skaggs Graduate School of Chemical and Biological Sciences. His research focuses on RNA structure, ribosome assembly, and the structural biology of RNA-protein complexes. Williamson has held leadership roles, including Dean of Graduate and Postdoctoral Studies at The Kellogg School of Science and Technology and Executive Vice President for Research and Academic Affairs at TSRI. Education: B.S. in Chemistry (Mount Union College, 1981); Ph.D. in Chemistry (Stanford University, 1988). Postdoctoral training at the University of Colorado with Thomas R. Cech and Stanford with Steven G. Boxer. Research Interests: The lab investigates RNA structure and function, ribosome assembly dynamics using cryo-EM and mass spectrometry, HIV RNA-protein interactions, and RNA-guided regulation of stem cell differentiation in C. elegans. Key techniques include biochemical assays, structural genomics, and single-molecule fluorescence methods. Awards: Multiple prestigious awards include the Camille Dreyfus Teacher-Scholar Award (1995), election to the American Academy of Arts and Sciences (2010), and the National Academy of Sciences (2022). Grants and Collaborations: Collaborations include work with David Millar, Clint Potter, and Ron Milligan on ribosome assembly. Research funded by NIH, HHMI, and other agencies. Labs: Williamson Lab at Scripps Research, focusing on structural biology and ribosome biogenesis.

Pengfei Fanمشاهده پروفایل

مدرس

• Computational Imaging
• Low-level Vision
• Image Restoration and Enhancement
• +۳ مورد دیگر

Dr. Pengfei Fan is a Lecturer in Data Science and AI at Queen Mary University of London (QMUL). He holds a PhD from QMUL (2021), focusing on learning-based imaging through dynamic scattering media. He previously served as an Assistant Professor at Xi'an Jiaotong-Liverpool University (2022–2024) and as a Research Associate at Nanjing University of Science and Technology (2021–2022). He also held a Visiting Researcher position at Tsinghua University (2020). His research interests span computational imaging, low-level vision, and medical imaging applications. He has secured grants such as the President and Principal’s Fund for Educational Excellence (2024) and collaborates with organizations like EPSRC, Royal Society, NSFC, and Jiangsu Science and Technology Programme. Pengfei is an active member of academic societies (IEEE, IET, BMVA, OSA, SPIE, IAPR) and holds Fellow status with the Higher Education Academy (FHEA). He contributes to educational initiatives via roles at QMUL-BUPT Joint Teaching and Learning Centre and the Centre for Excellence in AI in Education. His recent work emphasizes diffusion models, medical image segmentation, and competition-based learning in education. He welcomes PhD applications through CSC, CONACYT, and HEC.

Natalia Bulgakovaمشاهده پروفایل

مدرس

• Cell Biology
• Molecular Biology
• Developmental Biology
• +۶ مورد دیگر

Dr. Natalia Bulgakova is a Lecturer in Genetics, Genomics and Fundamental Cell Biology at the School of Biological and Behavioural Sciences, Queen Mary University of London. She maintains dual affiliations with the Centre for Molecular Cell Biology and the Centre for Evolutionary and Functional Genomics, where she leads an active research laboratory exploring fundamental mechanisms of cell adhesion and tissue organization. Her research program centers on understanding the roles and regulation of intercellular adhesion during organism development, with particular focus on E-cadherin dynamics. Dr. Bulgakova's laboratory employs a sophisticated multidisciplinary approach combining Drosophila melanogaster genetics with human cell culture systems to investigate how E-cadherin, the major component of intercellular adhesion, is regulated through intracellular trafficking and transcriptional mechanisms. Her work examines how alterations in these processes impact critical cell behaviors including proliferation, survival, and signaling in both normal development and pathological conditions. Methodologically, her research integrates classical genetic approaches with cutting-edge technologies including state-of-the-art microscopy (quantitative confocal imaging, live imaging, Airyscan, STORM) and genomic/epigenomic analyses (RNA-seq, ChIP-seq). Recognizing the complexity of data generated by these techniques, her laboratory develops custom computational pipelines for automated analysis of imaging and sequencing datasets, ensuring reliable and reproducible extraction of biological insights. Analysis of her publication record from 2013-2023 reveals a cohesive research trajectory focused on cell adhesion mechanisms, particularly E-cadherin regulation, and its relationship to cytoskeletal organization, epithelial morphogenesis, and developmental patterning. Her work spans multiple model systems including Drosophila, human cell lines, and Tetrahymena, demonstrating both depth in specific mechanisms and breadth across biological contexts. Cell adhesion mechanisms Cytoskeleton organization and dynamics Intracellular protein trafficking Drosophila developmental genetics Quantitative image analysis Epithelial morphogenesis E-cadherin regulation networks Dr. Bulgakova's laboratory represents a vibrant research environment that bridges molecular, cellular, and tissue-level phenomena to understand fundamental mechanisms of development and tissue organization. Her work has significant implications for understanding not only basic developmental processes but also pathological conditions where cell adhesion is disrupted.

Viji Draviam Sastryمشاهده پروفایل

استاد

• Cell Division
• Cytoskeleton Dynamics
• Genomic Instability
• +۳ مورد دیگر

Professor Viji Draviam Sastry is a Professor of Quantitative Cell and Molecular Biology and Director of Industrial Innovation at Queen Mary University of London. She leads the Draviam Lab, focusing on cell division mechanisms, cytoskeletal dynamics, and genomic stability. Her work integrates high-resolution microscopy, molecular biology, and computational tools to study chromosome segregation errors and their implications in cancer. Draviam holds a PhD from Trinity College, Cambridge, and completed postdoctoral research at MIT and Harvard Medical School. Her research interests include mitotic spindle dynamics, kinetochore-microtubule interactions, and the development of automated imaging tools like SpinX. She collaborates with industry partners such as Zeiss and Image Solutions to advance drug discovery and precision medicine. Key achievements include identifying novel chromosomal instability aiding variants (CIVa) and elucidating mechanisms regulating chromosome attachment stability. Draviam supervises multiple PhD students and has contributed to over 60 publications, including work on Astrin-PP1 pathways and MARK2 kinase roles in spindle positioning. She chairs the BBSRC LIDo-DTP program and co-leads AI-driven drug discovery initiatives. Her lab’s interdisciplinary approach bridges cell biology, bioinformatics, and engineering to address fundamental and translational questions in cell division.

Daniel Angمشاهده پروفایل

پژوهشگر

• Quantum Sensing
• Precision Measurement
• Quantum Diamond Microscopy
• +۴ مورد دیگر

Daniel Ang is a Researcher and staff scientist at the Quantum Technology Center (QTC) of the University of Maryland, working under Prof. Ron Walsworth. He leads efforts in quantum sensor development for directional detection of dark matter and neutrinos using nitrogen-vacancy (NV) centers in diamond. Previously, he completed a PhD in atomic physics at Harvard University, contributing to the ACME EDM experiment to measure the electron's electric dipole moment. Education: PhD in Atomic Physics, Harvard University (2023) Bachelor's in Physics, Amherst College (2015) Research Focus: Quantum sensing with NV centers for dark matter/neutrino detection Development of quantum materials (hexagonal boron nitride, silicon carbide) Super-resolution microscopy and strain sensing techniques Machine learning-driven molecular dynamics simulations ACME Experiment Contributions: Optimized ThO molecule lifetime measurements Improved photodetector and data acquisition systems Enhanced magnetic field control for EDM sensitivity Current Projects: 3-stage dark matter detector design using diamond segments 3D quantum diamond microscopy for nanoscale track imaging Collaboration with Sandia National Lab on ion-implanted damage tracks Labs/Teams: Quantum Technology Center (QTC) at University of Maryland, Walsworth Group.

• Computer Graphics
• Medical Imaging
• Virtual Reality
• +۳ مورد دیگر

Klaus Hinrichs is a Professor at the University of Münster. His work focuses on computer graphics, medical imaging, virtual reality, and scientific visualization. He leads research in interactive visualization techniques, geometric algorithms, and human-computer interaction, with applications in medical diagnostics, astrophysics, and immersive environments. His research interests include 3D reconstruction, volume rendering, gesture-based interfaces, and redirected walking in VR. He has contributed to projects like VIPAR for histopathology analysis and the Voreen framework for interactive volume visualization. Recent work explores touch interaction with stereoscopic objects, haptic feedback systems, and biometric authentication methods for mobile devices. His publications span over 30 years, emphasizing interdisciplinary approaches to visualization and user interface design.

Yichen Dingمشاهده پروفایل

استادیار

• Machine Learning
• Optical Microscopy
• Medical Imaging Technologies
• +۵ مورد دیگر

Yichen Ding is an Assistant Professor in the Department of Bioengineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on integrating advanced microscopy techniques like light-sheet and light-field imaging with machine learning and virtual reality to study cardiovascular structure, function, and regeneration. Specific applications include analyzing cardiac contractility in zebrafish models, myocardial deformation in hypertrophic hearts, and vascular network dynamics in injury/repair scenarios. Education: Ph.D. in Biomedical Engineering from Peking University (2015), B.S. in Precision Instrumentation from Tsinghua University (2010). His work bridges engineering and medicine, emphasizing multi-scale imaging technologies and computational tools for biomedical discovery. Recent projects involve developing VR tools for surgical planning and real-time volumetric imaging of cardiac mechanics. Key research themes include: Light-sheet microscopy for 4D cardiac imaging Machine learning for medical image segmentation/analysis Biomechanical modeling of cardiovascular systems Cardiac regeneration mechanisms in zebrafish/larval models Publications consistently address imaging innovation, cardiovascular pathophysiology, and computational approaches to biomedical problems. Current efforts emphasize translating imaging technologies into clinical applications via extended reality platforms.

Jean-Christophe Olivo-Marinمشاهده پروفایل

استاد

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +۱۳ مورد دیگر

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.