جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Medical Imaging
• Neural Networks
• +۵ مورد دیگر

Qing Wu is a faculty member at ShanghaiTech University's School of Information Science and Technology. Their research focuses on machine learning, medical imaging, neural networks, optimization algorithms, and computer vision. They have contributed to advancements in areas like deep learning for medical diagnostics, image segmentation, and wireless network design. Research Interests: Development of neural network architectures for medical imaging tasks (e.g., MRI/CT reconstruction, retinal vessel segmentation) Application of deep learning in genomics and pharmacology for disease prediction and drug discovery Optimization of wireless communication systems and network architectures Graph-based algorithms for data clustering and representation learning Recent Research Trends: Publications from 2024-2025 emphasize multi-modal medical imaging solutions using CNNs and diffusion models, alongside contributions to robust object detection and graph clustering techniques. Work spans healthcare AI, electromagnetic theory, and agricultural technology applications. Labs/Teams: Active in research groups focusing on AI-driven medical diagnostics, wireless systems innovation, and computational biology. Collaborates with interdisciplinary teams across computer science, electrical engineering, and biomedical fields.

• Structural Reliability Engineering
• Power System Resilience
• Seismic Risk Assessment
• +۳ مورد دیگر

Qiang Xie is a Professor in the Department of Electrical Engineering at Zhejiang University's College of Engineering. With an active research career spanning over two decades, Dr. Xie has established himself as a leading researcher in structural reliability engineering with significant contributions to power system resilience and seismic risk assessment. Dr. Xie's research interests center on structural reliability engineering with particular focus on power infrastructure resilience. His work bridges civil and electrical engineering domains, developing innovative frameworks for seismic risk analysis of electrical substations, post-earthquake power system restoration, and wind-related risk assessment for transmission systems. In recent years, he has expanded his research into medical imaging applications, particularly in gastrointestinal endoscopy assistance systems and ultrasound bone imaging. Analysis of Dr. Xie's recent publications (2021-2026) reveals a strategic expansion of research scope while maintaining core expertise in reliability engineering. The earliest works focused on wireless sensor networks and computer science applications, while the 2018-2021 period saw a strong emphasis on medical imaging and bioinformatics. Since 2022, there's been a pronounced return to power system resilience with increasingly sophisticated methodologies incorporating Bayesian networks, copula models, and multi-strategy frameworks. This evolution demonstrates both depth in core competencies and adaptability to emerging interdisciplinary opportunities. Multiple publications in Reliability Engineering & System Safety (6 papers 2022-2026) Contributions to medical imaging in IEEE Transactions and Frontiers journals Collaborative work with international researchers across engineering and medical domains Dr. Xie maintains active collaborations across multiple institutions, as evidenced by his diverse co-authorship patterns. His research demonstrates strong translational potential, particularly in critical infrastructure protection and medical diagnostic technologies. While specific grant information isn't available in the publication record, the consistent output and diverse funding sources implied by publication venues suggest sustained research support.

Mauricio Rocha-Martinsمشاهده پروفایل

پژوهشگر

• Developmental Biology
• Cellular Plasticity
• Neuroscience
• +۳ مورد دیگر

Dr. Mauricio Rocha-Martins is a Researcher at the Max Planck Institute for Molecular Biomedicine in Münster, Germany, leading the Rocha Lab focused on Embryo Self-Correction . His work investigates how embryos overcome genetic and environmental stressors to develop functional organs, emphasizing developmental robustness, cellular plasticity, and nervous system development. Affiliations: Max Planck Institute for Molecular Biomedicine Role: Lab Head (Rocha Lab) and Supervisor in the CiM-IMPRS Graduate Programme Methodologies: Live imaging of in vivo systems, organoids, CRISPR genetics, light-sheet microscopy, and deep-learning image restoration Research Interests: His lab explores mechanisms enabling embryos to recover from developmental defects using advanced imaging and molecular profiling. Key areas include: Cellular cooperation under stress Regeneration of tissue organization Biophysical and molecular dynamics in repair processes Scientific Contributions: Publications span retinal development, neuronal migration, and imaging innovations. Recent work (2023-2025) highlights novel insights into progenitor cell behavior, neurogenic signaling, and stress adaptation in embryonic systems. Lab & Team: The Rocha Lab combines experimental and computational approaches, collaborating with the Cells in Motion initiative. Their work bridges basic research with potential applications in regenerative medicine and developmental disorder modeling.

• Deep Learning
• Audio/Video Content Analysis
• Biomedical Data Analysis
• +۳ مورد دیگر

Dr. Markus Mühling is a Research Fellow in the Department of Mathematics and Computer Science at Philipps-Universität Marburg, working under Prof. Bernd Freisleben. His research focuses on deep learning applications for audio/video content analysis and biomedical data interpretation. He joined the group in 2015 and has contributed to projects involving wildlife monitoring, medical imaging, and heritage digitization. His work spans sensor networks, neural network architectures, and edge computing solutions for real-world challenges. Key research areas include: Deep learning for bioacoustic and visual species recognition Automated analysis of biomedical and histological images Edge-based AI systems for resource-constrained environments Content-based retrieval in video and image archives Publications highlight advancements in biodiversity monitoring (e.g., acoustic sensor networks), medical imaging (e.g., blood cell counting), and cultural heritage (e.g., organ tablature transcription). His work bridges theoretical machine learning with practical ecological and biomedical applications.

• Deep Learning
• Image and Video Analysis
• Visual Similarity Search
• +۳ مورد دیگر

Nikolaus Korfhage is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Mathematics and Computer Science within the Faculty of Mathematics and Computer Science. He is part of Prof. Bernd Freisleben's research group focusing on Distributed Systems and Intelligent Computing. His research emphasizes deep learning applications in image and video analysis, particularly visual similarity search and biomedical data analysis. Korfhage initiated his PhD in 2016 under Prof. Freisleben’s supervision, exploring computational methods for multimedia data processing. His work spans diverse domains including avian physiology analysis, automated music notation transcription, and historical video archive retrieval. Notable projects include the VIVA initiative for content-based video search and the DeepTab system for transcribing organ tablature music. Recent publications highlight advancements in cell segmentation, bat echolocation analysis, and transformer-based species recognition. Korfhage’s contributions bridge computer science and interdisciplinary fields like biology and musicology. He collaborates actively on tools like ElasticHash for semantic image search and MESA for synthetic DNA assessment. His research underscores innovation in both technical methodologies and real-world applications across multimedia, biomedical, and ecological domains.

• Artificial Intelligence
• Semantic Web
• Knowledge Graphs
• +۷ مورد دیگر

James A. Hendler is a Professor at Rensselaer Polytechnic Institute, renowned for pioneering work in Artificial Intelligence, Semantic Web, and Web Science. His research spans knowledge graphs, explainable AI, and biomedical informatics Rensselaer Polytechnic Institute, Troy, NY, USA Research Interests : Hendler's work bridges AI and Semantic Web technologies, focusing on knowledge integration, quantum machine learning applications, and mental health prediction through NLP. He develops explainable systems for medical imaging and contributes to open government data frameworks Publications & Collaborations : Recent articles highlight collaborations in quantum ML for climate modeling, clinical decision support semantics, and chest X-ray pathology explainability. He explores active learning architectures and human-in-the-loop AI systems Scientific Recognition : AAAS Fellow ACM Fellow AAAI Fellow Advising & Grants : Co-authored over 100 publications with researchers in biomedical NLP, quantum computing, and social network analysis. Key grants include NIH and NSF funding for semantic health data projects

Sandesh Athni Hiremathمشاهده پروفایل

پژوهشگر

• Stochastic Processes
• Partial Differential Equations (PDEs)
• Mathematical Modeling
• +۵ مورد دیگر

Dr. Sandesh Athni Hiremath is a Researcher at the Department of Mechatronics in Mechanical Engineering and Vehicle Technology at RPTU Kaiserslautern-Landau, affiliated with the Chair of Mechatronics (MEC). He holds a PhD in Mathematics from TU Kaiserslautern (2015) and a Master's in Informatics from the same institution. His research focuses on stochastic processes, partial differential equations (PDEs), optimal control, computer vision, and multiscale mathematical modeling. He has professional experience as an algorithm developer at Valeo Schalter und Sensoren in Germany. His work spans interdisciplinary areas including mathematical biology (cancer invasion modeling), control systems, and autonomous driving technologies. Recent contributions include PDE-based depth estimation for fisheye cameras and resilience analysis in industrial control systems. His publications integrate theoretical frameworks with practical applications in robotics, computer vision, and biomedical systems. Research highlights include developing stochastic models of acid-mediated tumor invasion and exploring optimal control strategies in dynamic systems. He collaborates with institutions like TU Kaiserslautern and international conferences (IEEE, ICCSA). Current interests align with advancing multiscale modeling and real-world applications in mechatronics and vehicle technology.

Andrea Lukovaمشاهده پروفایل

پژوهشگر

• Human Evolution
• Functional Morphology
• Bipdealism Evolution
• +۴ مورد دیگر

Dr. Andrea Lukova is a Postdoctoral Researcher at the Max Planck Institute for Evolutionary Anthropology's Department of Human Origins. Her work focuses on reconstructing locomotor diversity in hominins through micro-CT analysis of lower limb bone internal morphology. She specializes in trabecular bone structure and its implications for bipedalism evolution, particularly studying the Homo naledi foot. Education: PhD in Anthropology (2024), University of Kent – Dissertation: Functional Morphology of the Hominin Knee MA in Anthropology of Past Populations (2020), University of West Bohemia – Dissertation on primate wrist trabecular bone BA in Social and Cultural Anthropology (2018), University of West Bohemia – Award-winning thesis on carpal evolution Research interests include primate locomotion, functional morphology, and evolutionary biomechanics. She has conducted fieldwork at the Portuguese Lapa do Picareiro site (Middle-Upper Paleolithic transition) and uses advanced software (Avizo, 3D Slicer, Medtool) for imaging and statistical analyses. Key awards: Recipient of the Faculty Dean’s Award for Outstanding Bachelor’s Thesis (2018). Her recent publications explore trabecular bone architecture in australopiths and hominins, emphasizing locomotor diversity and methodological innovations in imaging. Teaching experience includes postgraduate Biological Anthropology at University College London. Technical skills span 3D imaging, statistical software (R, SPSS), and advanced graphic design tools.

Fatemeh Taheriyanمشاهده پروفایل

پژوهشگر

• Medical Signal Processing
• Neuroimaging Data Analysis
• Neural Circuits
• +۳ مورد دیگر

Fatemeh Taheriyan is a Researcher at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany, affiliated with the Neural Circuits, Consciousness, and Cognition Group under Prof. Lucia Melloni. Her career includes roles as R&D Researcher at WTOM (Tehran, Iran), intern at the Donders Centre for Cognitive Neuroimaging (Nijmegen, Netherlands), and research positions in AI-Med and IPM (Institute for Research in Fundamental Sciences, Tehran). She holds an M.Sc. in Biomedical Engineering from Khajeh Nasir Toosi University of Technology (2015–2018) and a B.Sc. in Electrical Engineering from Shahrood University of Technology (2010–2014). Her research interests focus on medical signal/image processing, neuroimaging data analysis, and neural circuit dynamics. Recent work emphasizes MRI techniques for resting-state networks, computational neural models, and optimization in biomedical systems. No awards are explicitly listed, but her contributions span academic and industry research collaborations. She is involved in interdisciplinary projects at the intersection of neuroscience and technology.

Dieter Schmalstiegمشاهده پروفایل

استاد

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +۷ مورد دیگر

Dieter Schmalstieg is the Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart, Germany, and an adjunct professor at Graz University of Technology, Austria. He previously served as Head of the Institute of Visual Computing at TU Graz (2012-2023) and holds a habilitation in Computer Science from Vienna University of Technology. His career spans 30+ years of innovation in extended reality systems. Education: PhD in Computer Science (1997, TU Vienna, summa cum laude ), Dipl.-Ing. (1993, TU Vienna), Fulbright Postgraduate Studies (1994-1995, Stanford) Current Roles: Institute Director at Stuttgart, Adjunct Professor at TU Graz As a leading authority in visual computing , Schmalstieg's research focuses on augmented reality , virtual reality , 3D user interfaces , and medical visualization . His work bridges fundamental graphics algorithms with real-world applications in surgery, industrial maintenance, and environmental monitoring. Key contributions include: Real-time shading atlas rendering for VR Diminished reality systems for subsurface utility engineering Medical AR frameworks for cranial implant design Neural rendering techniques for focus cues Spatial interaction paradigms and collaborative XR His recent publications demonstrate AI integration in AR systems, real-time 3D reconstruction , and procedural content generation for immersive environments. With 400+ peer-reviewed publications and 29,000+ citations, Schmalstieg's work has shaped modern extended reality research. Scientific recognition includes: IEEE Fellow (2021) IEEE ISMAR Career Impact Award (2020) IEEE VR Technical Achievement Award (2012) START Prize (2002), Austria's highest research award for early-career scientists He has advised 40+ PhD/MSc students, many of whom became professors at top institutions. His research spans: Fundamental graphics algorithms (e.g., meshlet rendering, occlusion culling) Medical applications (surgical navigation, cranial reconstruction) Industrial AR (assembly guidance, maintenance systems) Immersive analytics (situated visualization, multi-layer displays) Mobile tracking (SLAM, panoramic localization)

Dmitry Kobakمشاهده پروفایل

استاد مهمان

• Machine Learning
• Data Science
• Biological Applications
• +۷ مورد دیگر

Dmitry Kobak is a group leader in the Department of Data Science at the Hertie AI Institute, University of Tübingen, Germany. He holds the title of Privatdozent at the Faculty of Computer Science and served as a visiting professor (Vertretungsprofessor) at Heidelberg University during the 2023/24 winter semester. His research focuses on machine learning and data science applications in biology, including self-supervised learning, dimensionality reduction, and topological data analysis. He is also engaged in statistical forensics, analyzing electoral fraud, war fatalities, and excess mortality patterns. Education: BSc in Computer Science (St. Petersburg ITMO University), MSc in Theoretical Physics (St. Petersburg State University), PhD in Computational Motor Control (Imperial College London). Postdoctoral work with the Machens Lab (Champalimaud Institute) and Mehring Lab (Freiburg University/Imperial College London). Teaching: Introductory machine learning courses for MSc students in Tübingen and BSc students in Heidelberg. Recent courses include Einführung ins Machinelle Lernen (German) and Transformers, Large Language Models, and their use in Physics (English). Research supervision includes postdocs (Sebastian Damrich), PhD students (Rita González Márquez, Niklas Böhm), and multiple MSc students. Active in reviewing for top venues like NeurIPS, ICML, and Nature journals. Labs/Teams: Member of the ELLIS Society, Cluster of Excellence «Machine Learning for Science», and IMPRS-IS associated scientist. His work bridges machine learning theory with practical applications in neuroscience, forensics, and biomedical research.

Alexander Southanمشاهده پروفایل

پژوهشگر

• Materials Science
• Polymer Chemistry
• Biomaterials
• +۳ مورد دیگر

Dr. Alexander Southan is a Senior Research Scientist in the CSF Materials department at the Max Planck Institute for Intelligent Systems since 2023. His research focuses on functional bio-based or synthetic hydrogels and polymers, addressing applications in sensors, drug delivery, and biomaterials. Previously, he led the 'Chemical and Physical Interfaces' group at the University of Stuttgart and was a team leader in the NanoBioMater interdisciplinary initiative, bridging chemistry, materials science, biology, and engineering. He holds a PhD in polymer chemistry from the University of Stuttgart (2014) and a diploma from Philipps-University of Marburg and Bangor University, specializing in macromolecular chemistry and advanced spectroscopic techniques like CARS. Research interests include polymer synthesis, cross-linking chemistry, hydrogel formulation, and their applications in environmental sensing, drug delivery systems, and biomedical engineering. Notable contributions include developing hydrogels for enzyme immobilization using viral scaffolds and optimizing material properties via design-of-experiments approaches. His work on 3D bioprinting standardization has advanced reproducibility in tissue engineering. He has also contributed to material science through software tools like pyPreprocessing for spectral data analysis and pyProtein for biopolymer calculations.

Anna Kashkanovaمشاهده پروفایل

پژوهشگر

• Interferometric Scattering Microscopy
• Biological Systems
• Nanoparticle Analysis
• +۳ مورد دیگر

Anna Kashkanova is a Postdoctoral Researcher at the Max Planck Institute for the Science of Light (MPL), affiliated with the Sandoghdar Department. Her research focuses on applications of interferometric scattering microscopy (iSCAT) in biological systems, particularly in nanoparticle analysis, live-cell imaging, and extracellular vesicle characterization. She has contributed to advancements in precision nanoparticle tracking and label-free detection techniques. Her academic background includes expertise in optical physics and biophysics, with a strong emphasis on developing microscopy-based tools for biomedical and nanoscale studies. Notable contributions include methods for measuring nanoparticle concentration in polydisperse mixtures and distinguishing extracellular vesicles from lipoproteins. Publications highlight her work on electrostatic force clamping of nanoparticles, 3D live-cell imaging, and the functional properties of extracellular vesicles in parasitic systems. Despite no listed scientific awards, her research is widely recognized in biophysics and nanotechnology circles. She collaborates actively with the Sandoghdar group and contributes to the Max Planck School of Photonics initiatives. Labs and teams: Active in the MPL's microscopy and nanotechnology research groups, focusing on interdisciplinary projects at the interface of optics and biology.

• Biomedical Imaging
• Optical Coherence Tomography (OCT)
• Nanoparticle Contrast Agents
• +۳ مورد دیگر

Muktesh Mohan is a Postdoctoral Fellow at the Max Planck Institute for the Science of Light, focusing on advancing optical coherence tomography (OCT) technologies for biomedical applications. His research integrates nanotechnology, materials science, and photonics to develop innovative contrast agents and imaging techniques. Key areas include molecular imaging with upconversion nanoparticles, real-time monitoring of biological processes (e.g., enzyme kinetics, tissue soldering), and in vivo/ex vivo studies of disease models such as inflammatory bowel disease and oesophageal tissue. His work spans interdisciplinary collaborations, addressing challenges in early disease detection, drug delivery systems, and functional OCT applications. He has contributed to the development of multi-model contrast agents and advanced image segmentation algorithms for high-resolution retinal imaging. Ongoing projects involve exploring nanomaterials for enhanced imaging contrast and theranostic applications. Mohank’s research also extends to biofilm analysis using OCT, with studies on wheat leaf infections and bacterial colony characterization. His contributions bridge fundamental science with translational biomedical engineering, aiming to improve clinical diagnostics and therapeutic monitoring.

Richard Taylorمشاهده پروفایل

پژوهشگر ارشد

• Nanophotonics
• Biological Nanotechnology
• Optical Microscopy
• +۴ مورد دیگر

Richard Taylor is a Research Fellow at the Max Planck Institute for the Science of Light, specializing in nanophotonics and interferometric scattering microscopy (iSCAT). He holds a Master's (1st Class Honors) in Physics from the University of Birmingham (2009) and a PhD from the University of Cambridge (2013), where his doctoral work focused on plasmonic nanoparticles and led to a patent for plasmonic junctions in surface-enhanced spectroscopy. Since 2013, he has worked in Prof. Vahid Sandoghdar's group, developing iSCAT microscopy for live-cell applications and became a Humboldt Postdoctoral Fellow in 2015. His research interests include visualizing nanoscale biological dynamics, particularly membrane organization and cellular function. He has pioneered tools like nanofluidic delivery systems for precise molecular manipulation in live cells. His work bridges plasmonics, microscopy innovation, and single-molecule biophysics. Education: BSc (Hons), Physics, University of Birmingham, 2009 PhD in Nanophotonics, University of Cambridge, 2013 Key research contributions include high-precision protein tracking, iSCAT microscopy development, and SERS-based analysis of nanoparticle clusters. He co-organized the inaugural international workshop on iSCAT microscopy in 2020, advancing scientific outreach and collaboration. Awards: Humboldt Postdoctoral Fellowship (2015) Grants/Advising: Not explicitly listed; focus on experimental and theoretical nanoparticle systems. His lab develops microscopy tools for studying biological systems at unprecedented spatiotemporal resolution, with applications in biophysics, nanomedicine, and materials science.