جست‌وجوی استاد و پژوهشگر

• Microbial Ecology
• Agricultural Microbiology
• Plant-Microbe Interactions
• +۳ مورد دیگر

Frank Dazzo is a Professor in the Department of Plant, Soil and Microbial Sciences at Michigan State University (MSU), affiliated with the College of Natural Science. He holds a Ph.D. from the University of Florida (1975) and completed an NSF Postdoctoral Fellowship at the University of Wisconsin (1976-1977). His research focuses on beneficial plant-bacteria associations, particularly nitrogen-fixing Rhizobium species, and the development of CMEIAS software for microbial ecological analysis. Education: B.S., Florida State University (1970) M.S., University of Florida (1972) Ph.D., University of Florida (1975) NSF Postdoctoral Fellow, University of Wisconsin (1976-1977) Research Interests: Dr. Dazzo investigates symbiotic plant-microbe interactions, including nitrogen fixation in legumes and cereals. His work explores novel associations between Rhizobium and plants like rice and wheat, aiming to enhance agricultural productivity. He also develops CMEIAS, a computational tool integrating microscopy and image analysis for studying microbial communities at single-cell resolution. Publications: His research spans over 50 peer-reviewed articles, focusing on microbial ecology, plant-microbe symbiosis, and computational tools. Recent work includes studies on biofilm communication, alpine lake Dinophyceae fluctuations, and rice microbiome dynamics under fertilizer treatments. Awards/Grants: While no specific awards are listed, his extensive publication record and NSF postdoctoral fellowship highlight sustained academic contributions. His work has been supported through grants for CMEIAS development and agricultural microbiology research. Labs/Teams: He leads the CMEIAS software development initiative and collaborates extensively on microbial ecology projects, including studies at MSU and international partnerships with institutes in Italy, China, and Germany.

William Smithمشاهده پروفایل

استاد

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

William Smith is a Professor in the Department of Molecular, Cellular & Developmental Biology at the University of California, Santa Barbara (UCSB). He earned his undergraduate and graduate degrees in Biology from UC Santa Cruz, followed by postdoctoral research at the University of Strasbourg (France) and UC Berkeley. His laboratory focuses on understanding chordate nervous system development and function, using the amphibian Xenopus and tunicate Ciona as model organisms. Key research areas include neural tube closure mechanisms, sensorimotor circuitry, and the evolution of central nervous system structures. Smith's work has advanced understanding of how T-type calcium channels regulate neural tube closure in both Ciona and vertebrates. Recent studies explore glutamate receptor roles in Ciona's sensorimotor circuits and the integration of visual and gravitactic behaviors. His lab employs cutting-edge techniques in neuroimaging, computational biology, and behavioral analysis. Collaborations with groups like the Interdepartmental Graduate Program in Dynamical Neuroscience at UCSB highlight his interdisciplinary approach. Publications since 2024 investigate deep homologies in chordate CNS structures and reveal conserved neural circuit architectures. His research bridges developmental genetics, molecular biology, and neurophysiology, contributing to both basic science and evolutionary biology. The Smith Lab is actively engaged in training graduate students and postdoctoral researchers in these areas.

Po-Yen Tungمشاهده پروفایل

عضو هیئت علمی

• Materials Science
• Machine Learning in Materials Discovery
• Electron Microscopy
• +۵ مورد دیگر

Dr. Po-Yen Tung is a Research Associate at the University of Cambridge's Department of Earth Sciences, jointly appointed with Materials Science and Metallurgy. He holds a PhD from the Max Planck Institute under Prof. Dirk Raabe and was a postdoc at Cambridge from 2021–2024, affiliated with Peterhouse College. His work bridges materials science and machine learning, focusing on accelerating materials discovery and microscopy analysis. Research Interests: Machine learning in materials characterization and alloy design High-entropy Invar alloy discovery Automated electron microscopy data analysis 3D microstructure segmentation Key Contributions: Developed SIGMA, an open-source tool for SEM-EDS analysis Discovered high-entropy Invar alloys using active learning frameworks Pioneered few-shot learning for 3D tomographic segmentation Publications highlight data-efficient material design, planetary science insights, and environmental magnetism studies. His work supports sustainable materials development and net-zero energy applications through AI-driven methods.

• Amyloid Formation
• Neurodegenerative Diseases
• Protein Aggregation
• +۴ مورد دیگر

Wolfgang Hoyer is a Junior Professor (equivalent to Assistant Professor) at Heinrich-Heine-University Düsseldorf and Forschungszentrum Jülich, leading the Institute for Physical Biology within the Faculty of Mathematics and Natural Sciences. His research focuses on mechanistic studies of amyloid formation in neurodegenerative diseases using biophysical, biochemical, and structural biology approaches. Research Interests: Dr. Hoyer investigates amyloid aggregation pathways, transient oligomeric states, and fibril polymorphs. Key areas include structural motifs in intrinsically disordered proteins (e.g., amyloid-β, α-synuclein, IAPP), inhibition strategies using engineered beta-wrapin proteins, and modulation of aggregation through molecular recognition features. His work bridges biophysics, neurobiology, and therapeutic development. Recent Research Trends: Publications emphasize amyloid fibril structure determination (cryo-EM), kinetic analyses of aggregation, cross-amyloid interactions, and engineered inhibitors targeting nucleation. Consistent themes include β-hairpin motifs, toxicity modulation, and mechanistic insights into Alzheimer's, Parkinson's, and diabetes-related amyloidosis. Awards and Honors: ERC Consolidator Grant (2016) Ulrich Hadding Research Prize (2016) Otto Hahn Medal (2004) DFG Research Stipend (2008) Kekulé Stipend (2001) Research Team: Leads a group with four PhD students (Tatsiana Kupreichyk, Marie Schützmann, Celina Schulz, Tina Jacob). Notable former member Emil Dandanell Agerschou received the Faculty's Best PhD Thesis Award (2021). The team collaborates with Forschungszentrum Jülich (IBI-7) and utilizes advanced techniques including TIRF microscopy and structural biology.

• Cloud computing
• Cybersecurity
• Software engineering
• +۹ مورد دیگر

Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.

• Energy Storage
• Battery Technology
• Materials Science
• +۲ مورد دیگر

Francois Usseglio Viretta is a Researcher in the Mechanical Engineering department at the Center for Energy Conversion and Storage Systems within the National Renewable Energy Laboratory . His work focuses on advanced energy storage technologies, particularly lithium-ion and solid-state batteries, with an emphasis on materials science, computational modeling, and electrochemical stability. Research Interests: Energy Storage, Battery Technology, Materials Science, Mechanical Engineering, Electrochemistry Labs/Teams: Center for Energy Conversion and Storage Systems Scientific Trends: Recent publications highlight his contributions to electrode microstructure analysis, segmentation modeling for microscopy, and multi-scale cathode design. His work also explores battery recycling processes and solid-state cathode processing techniques.

Frank Haußerمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Frank Haußer is a Professor at Beuth University of Applied Sciences Berlin in the Department II Mathematics - Physics - Chemistry. He serves as the academic advisor for the Applied Mathematics program and teaches courses including Numerical Mathematics, Mathematical Methods of Digital Image Processing, and Computational Engineering. His consultation hours are held during semesters and by appointment, with availability both in-person and online. Haußer's research focuses on: Numerical mathematics and scientific computing Mathematical modeling with applications in MATLAB/Octave Machine learning for medical imaging and insect monitoring Digital image processing techniques for biomedical applications Partial differential equations and computational engineering methods He has authored a textbook on mathematical modeling with MATLAB/Octave and leads interdisciplinary projects at the intersection of mathematics and technology. Analysis of his 15 most recent publications shows strong emphasis on: Medical imaging algorithms (especially retinal OCT analysis) Nanostructure dynamics and material science Computational physics and finite element methods Machine learning applications in biology and medicine Advanced mathematical modeling techniques His work consistently bridges theoretical mathematics with practical engineering applications. Haußer actively supervises student research, including: Machine learning for insect classification and localization Medical image processing algorithms Computational methods in engineering Finite element analysis applications Optimization and simulation techniques He has guided over 30 bachelor's and master's theses since 2009. His research projects include: KInsekt (2020-2023): AI-based insect monitoring funded by BMUV BeCRF (2015-2017): Medical image quality validation funded by BMWi QM ROCT (2013-2015): Automated OCT quality management funded by IFAF These interdisciplinary collaborations involve institutions across Germany.

Sebastian Schmelzleمشاهده پروفایل

پژوهشگر

• Functional Morphology
• Animal Morphology
• Arthropod Structure
• +۵ مورد دیگر

Dr. Sebastian Schmelzle is a researcher at the Department of Biology, Technische Universität Darmstadt , where he works on functional morphology and biomechanics of arthropods. Since 2022, he has also served as CEO of Small World Vision GmbH . His academic career includes a magna cum laude PhD in Biology (2015) and research roles in DFG- and BMBF-funded projects like LIS, Waldklimafonds, NOVA, and ASTOR. He specializes in 3D imaging techniques, particularly synchrotron microtomography , and has contributed to open-source platforms like Biomedisa . Education : PhD in Biology (2015, TU Darmstadt), Diploma in Biology (2008, Eberhard Karls University Tübingen) Research interests focus on arthropod defensive mechanisms , notably the ptychoid defense in oribatid mites. His work explores how evolutionary lineages independently develop exoskeletal and muscular adaptations for survival. He bridges entomology , materials science , and computational imaging to study biomechanics, surface chemistry, and structural efficiency. Publications highlight his expertise in 3D microtomography (e.g., analyzing ant mandibles, insect cuticular hydrocarbons, and primate retinal structures). Collaborative projects span biomedical image segmentation , insect biomechanics , and synchrotron radiation applications . His recent work integrates machine learning and real-time data filtering for enhanced visualization. Additional contributions include public engagement initiatives like the AR Museum Station and Insect Excursions , aiming to democratize scientific knowledge through interactive exhibits and field studies.

Yuling Yanمشاهده پروفایل

استاد

• Biomedical Imaging
• Machine Learning for Medical Applications
• Biosignal Analysis
• +۷ مورد دیگر

Yuling Yan serves as Professor and Founding Chair of the Department of Bioengineering at Santa Clara University's School of Engineering, holding the Phil and Bobbie Sanfilippo Professorship. She maintains an affiliation as Consulting Faculty at Stanford University's Department of Otolaryngology. Her academic journey includes faculty positions at the University of Hawaii-Manoa (2002-2005), University of Wisconsin-Madison, and University of the Ryukyus (1997-1999), with postdoctoral training at McGill University and Max Planck Institute for Biochemistry. Professor Yan's research focuses on AI-driven medical diagnostics, specializing in machine learning applications for early disease detection including vocal pathologies, cardiac arrhythmia, skin cancer, brain aneurysms, and breast cancer. Her work integrates biomedical imaging (MRA, ultrasound, CT), biosignal analysis, and fluorescence microscopy with molecular switch probes. Current projects involve collaboration with radiologists at Santa Clara Valley Hospital to develop deep learning systems for medical image analysis. Her publication trends reveal an evolution from foundational work in vocal fold dynamics and optical probe development (2011-2015) toward AI/ML applications in medical diagnostics (2017-present), with increasing emphasis on deep neural networks for cancer detection and cardiac monitoring. Key contributions include novel methodologies for high-speed laryngeal imaging analysis and optical lock-in detection microscopy. Active research funding includes NIH Multi-Investigator Awards for high-contrast single-molecule imaging and NSF grants for laryngeal imaging tools development. She teaches graduate courses including Machine Learning for Biomedical Applications and Deep Learning for Medical Image Analysis, alongside undergraduate bioengineering fundamentals.

María Virumbrales-Muñozمشاهده پروفایل

استادیار

• Microphysiological Systems
• Organ-on-a-Chip
• Cancer Biology
• +۱۲ مورد دیگر

María Virumbrales-Muñoz is an Assistant Professor in the Department of Obstetrics and Gynecology at the University of Wisconsin–Madison, with an affiliate appointment in Biomedical Engineering. She is also the Principal Investigator of the Virumbrales-Muñoz Lab, where she leads research in microphysiological systems to model cancer and reproductive disorders. Position: Assistant Professor, Obstetrics & Gynecology Affiliation: Biomedical Engineering (Affiliate Faculty) Lab: Virumbrales-Muñoz Lab, University of Wisconsin–Madison Email: virumbralesm@wisc.edu She holds a PhD in Biomedical Engineering from the University of Zaragoza, Spain, and completed postdoctoral training at UW-Madison in Biomedical Engineering and Pathology & Laboratory Medicine. Her research lies at the intersection of bioengineering and women's health, focusing on 3D biomimetic modeling of diseases such as high-grade serous ovarian cancer (HGSOC) and preeclampsia. Using microfluidic technologies, her lab develops organ-on-a-chip models to study tumor microenvironments, metabolic heterogeneity, immune-endothelial interactions, and vascular dysfunction in reproductive diseases. The lab's recent publications reveal a strong focus on advancing microphysiological systems for cancer and vascular biology, with applications in drug screening, immune response modeling, and patient-specific disease modeling. Her work integrates molecular biology, bioengineering, and clinical data to improve understanding of disease mechanisms and therapeutic responses. Scientific Awards: Chester B. Martin Graduate Training Program Mentorship Award (2021) Dr. Virumbrales-Muñoz is an active mentor and educator, advising graduate and undergraduate students in biomedical engineering and reproductive sciences. She teaches in courses such as AN SCI 875 and OBS&GYN 710 and collaborates on major research grants, including NIH R01 proposals. Her lab is funded by the American Cancer Society, University of Wisconsin Carbone Cancer Center, and Meriter Foundation, among others. Research Labs and Teams: Virumbrales-Muñoz Lab: Focuses on microphysiological systems for cancer and preeclampsia Collaborations: UW-Madison Department of Biomedical Engineering, Pathology & Laboratory Medicine, and UnityPoint Health-Meriter Foundation Team Members: Includes postdoctoral researchers, PhD students, and research technicians working on ovarian cancer, breast cancer, and vascular modeling projects

• Machine Learning
• Bioinformatics
• Biomedical Applications
• +۶ مورد دیگر

Marc Vaisband is a researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, where he pursues his PhD under Prof. Hasenauer. His research focuses on machine learning applications in biomedical imaging and oncology, supported by funding from the Salzburg Cancer Research Institute. Education B.Sc. and M.Sc. in Mathematics at the University of Bonn Specialized in stochastics and numerical analysis, with theses on KPZ theory and Totally Asymmetric Simple Exclusion Process Research Interests His work bridges machine learning and biomedical challenges, particularly in age-related macular degeneration diagnostics, cancer therapy optimization, and stochastic modeling. He develops neural networks for medical imaging and survival analysis in hematologic malignancies. Scientific Contributions Marc's 15 most recent publications include machine learning-driven AMD detection via fluorescence microscopy, precision dosing in anticancer therapy, and deep learning validation for NGS genetic variants. His articles emphasize statistical modeling, image analysis, and clinical outcome prediction. Professional Context As part of the Hasenauer Lab, Marc contributes to computational medicine projects. His interdisciplinary approach integrates mathematical rigor with clinical applications.

Antoni Jaume-i-Capóمشاهده پروفایل

استاد

• Artificial Intelligence
• Explainable Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Dr. Antoni Jaume-i-Capó is a Full Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB) , affiliated with the Computer Graphics and Vision and Artificial Intelligence Group (UGiVIA) and serving as Director of the Artificial Intelligence Applications Laboratory (LAIA@UIB) . His work bridges Artificial Intelligence , Computer Vision , and Medical Imaging with applications in Motor Rehabilitation and Explainable AI (XAI) . Research Interests : Artificial Intelligence, Explainable AI, Computer Vision, Human-based Computation, Intelligent Systems for Motor Rehabilitation, Medical Imaging Processing. Teaching : Courses include Algorithmics and Data Structures , Final Degree Projects , and Practical Placements for Informatics Engineering degrees across multiple campuses. Scientific Contributions span 15 recent publications focusing on XAI fidelity metrics , medical image analysis , and crowdsourced diagnostics . These works integrate machine learning for Sickle Cell Disease classification and kinect-based rehabilitation systems . Scientific Awards : Premio al mejor trabajo JENUI 2010 Students : Supervised over 11 PhD/Master’s students, including Gabriel Moyà-Alcover and Pedro Marrero-Fernández. Projects : Leads EU-funded initiatives like EXPLAINME and EUGAIN , alongside regional efforts such as People4Sicklemia .

Konstanze F. Winklhoferمشاهده پروفایل

استاد

• Neurodegeneration
• Ubiquitin system and proteostasis
• Mitochondrial biology and stress signaling
• +۴ مورد دیگر

Professor Konstanze F. Winklhofer is a full Professor of Molecular Cell Biology at the Institute of Biochemistry and Pathobiochemistry, Faculty of Medicine, Ruhr University Bochum, Germany. She additionally serves as Vice-Speaker of the Research Department of Neuroscience and heads the Winklhofer Lab, which is equipped with state-of-the-art imaging core facilities including super-resolution microscopy, lattice-SIM, holotomography, and high-content screening platforms. Education & Career: Current: Professor (W3) at Ruhr University Bochum, Faculty of Medicine, Institute of Biochemistry and Pathobiochemistry. Role: Vice-Speaker, Research Department of Neuroscience. Research Focus: The Winklhofer laboratory investigates molecular, cellular, and systems-level mechanisms underlying neurodegenerative diseases. Central themes include: Ubiquitin system & proteostasis: understanding how linear ubiquitination and E3 ligases such as Parkin and LUBAC control neuronal integrity. Mitochondrial biology: deciphering mitochondria-centric stress response pathways, inter-organellar communication, and innate immune signaling (NF-κB axis). Phase separation & condensate biology: studying how proteins such as NEMO, α-synuclein, and prion protein undergo liquid-liquid phase separation and how this impacts aggregation and clearance by autophagy. Advanced imaging: exploiting super-resolution microscopy (SIM, PALM/dSTORM), live-cell lattice-SIM, FRAP/FRET, and holotomography to visualize dynamic cellular processes in real time. Publications & Trends: Across more than 150 peer-reviewed articles since 2000, the group has progressively shifted from elucidating basic ubiquitin biochemistry to integrating these insights with emerging concepts of phase separation and mitochondrial signaling hubs. Recent high-impact works (2023-2025) in Nature Communications , EMBO Journal , Cell , and Life Science Alliance highlight novel roles for NEMO, PACRG, and LUBAC in aggregate clearance and neuroinflammation. Scientific Awards: No specific awards detailed in the provided text. Funding & Collaborations: The lab is funded by the German Research Foundation (DFG) through major consortia including: RESOLV Cluster of Excellence (EXC 2033) SPP2453 “Integration of mitochondria into the cellular proteostasis network” FOR2848 “Nanoscale Architecture and Heterogeneity of the Mitochondrial Inner Membrane” RTG2862 “Monoaminergic Neuronal Networks and Disease” IMPRS for Living Matter Lab & Imaging Core: The Winklhofer group operates an open-access Imaging Facility housing Zeiss Elyra PS.1, Elyra7 Lattice-SIM, Nanolive CX-A holotomography, Cytation 5 MPW multimode reader, and Sapphire Biomolecular Imager. Custom pipelines in Imaris and CellProfiler enable advanced segmentation, particle tracking, and machine-learning-assisted analysis.

Audrey Sallesمشاهده پروفایل

پژوهشگر

• Cell Biology
• Immunology
• Super-Resolution Microscopy
• +۲ مورد دیگر

Audrey Salles is a senior expert research engineer at the Unit of Technology and Service Photonic BioImaging (UTechS PBI) within Institut Pasteur in Paris, France. Since 2014, she has specialized in super-resolution microscopy and fluorescence correlation spectroscopy (FCS) techniques, supporting researchers through training and collaborative projects. Her work bridges biophysical modeling with advanced imaging to study plasma membrane mechanics and pathogen interactions. Education: PhD in Immunology (2011), CIML, Marseille, France Her research focuses on plasma membrane organization , ESCRT machinery in cell division , and actin cortex dynamics during viral budding . She collaborates on projects involving Saccharolobus islandicus cytokinesis, HIV-1 release mechanisms, and Staphylococcus aureus pathogenesis. Her publications span Nature Methods , Developmental Cell , and eLife , reflecting interdisciplinary applications of microscopy. Audrey leads quality assurance for the ISO9001 certification at UTechS PBI, ensuring rigorous standards in a facility that integrates equipment management, repair logistics, and staff upskilling. Her methodological innovations include enhanced super-resolution radial fluctuation (eSRRF) and three-dimensional live-cell imaging frameworks.

• Biomedical Engineering
• Medical Image Processing
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Marek Wodziński serves as a Lecturer at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His academic role involves teaching and research at the intersection of engineering and medical diagnostics, with a focus on developing computational solutions for clinical imaging challenges. Contact details include office B-1 room 205, phone +48 12 617 27 11, and email wodzinski@agh.edu.pl. Education: PhD in Engineering (indicated by 'dr inż.' title) His research centers on biomedical image processing and artificial intelligence applications in healthcare, with particular emphasis on medical and veterinary image analysis. Key areas include segmentation of anatomical structures in CT/MRI, explainable AI for diagnostic systems, multimodal assessment of neurodegenerative diseases (especially Parkinson's), and development of large-scale medical datasets. His work bridges computer vision, deep learning, and clinical practice to address real-world diagnostic limitations. Recent publications (2025) demonstrate a cohesive research trajectory focused on advancing AI interpretability in medical imaging, with significant contributions to veterinary radiography, cranial defect reconstruction, and Parkinson's disease assessment through voice/gait analysis. His participation in international challenges like MICCAI, SPPIN, and Fusemycells highlights practical implementations of his work, often involving multimodal data fusion and novel deep learning architectures for segmentation and classification tasks. Scientific Awards: No scientific awards mentioned in available information While specific student names and grant details aren't provided, his extensive publication record in top medical imaging venues indicates active mentorship of graduate researchers and involvement in collaborative international projects. His work frequently appears in challenge competitions, suggesting funding from research consortia focused on AI-driven medical diagnostics. Future directions likely include expanding multimodal approaches for neurodegenerative disease tracking and enhancing cross-species applicability of veterinary AI systems. He collaborates within the Department of Metrology and Electronics' biomedical research group, contributing to projects involving mixed reality diagnostic platforms, cranial implant design, and large-scale medical datasets like MedShapeNet. His team actively participates in global initiatives such as the MICCAI society challenges, focusing on reproducible AI solutions for clinical imaging problems.