جست‌وجوی استاد و پژوهشگر

Tino Nollمشاهده پروفایل

پژوهشگر

• Precision Mechanics
• X-ray Technology
• Nanostructures
• +۳ مورد دیگر

Dr. Tino Noll is a Scientist and Senior R&D Engineer at the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy , leading projects focused on precision mechanics for advanced X-ray and XFEL experiments. He is a member of Projects 3.2 (Solids and Nanostructures) and 3.3 (Transient Structures and Imaging with X-Rays). His work emphasizes developing mechanical systems achieving nanometer precision, such as mirror systems for BESSY and European XFEL facilities. Education: 2003: Ph.D., Technische Universität Berlin ("Elastische parallelkinematische Führungsgetriebe für ultrapräzise Bewegungen im Vakuum") 1989: Diplomingenieur (M.Sc. Machine Tool Design), Technische Universität Dresden Research Interests: Innovations in precision mechanics for high-demand applications, including nanoradian-precision mirror systems, split-and-delay line units for XFEL experiments, and X-ray scattering instrumentation. His work bridges mechanical design, simulation, and experimental physics to advance cutting-edge experiments in materials science and magnetism. Publications: Recent work spans topics like plasmonic nanostructure dynamics, tabletop X-ray setups, and high-speed imaging techniques. These reflect a focus on advancing X-ray technologies and precision engineering for ultra-fast, high-resolution experiments. Labs & Projects: Leads development of the Split & Delay Line for the Materials Imaging and Dynamics Experiment (MID) at European XFEL. Collaborates on experiments like VekMag, RICXS, and DynaMaX at BESSY, emphasizing custom mechanical solutions for novel scientific challenges.

• Nonlinear Optics
• Short Pulse Spectroscopy
• Nanostructured Materials
• +۳ مورد دیگر

Erik Guehrs is a researcher affiliated with the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy (MBI). His work focuses on advanced imaging techniques, including X-ray holography, nanotechnology, and biomedical applications. He contributed to studies on phase transformations in materials, nanoparticle interactions with biological systems, and magnetic heterostructures. Notably, his research addresses challenges in soft X-ray transparency for thin films and signal optimization in spectroscopic methods. While at MBI, he collaborated on projects involving ultrafast spectroscopy and coherent imaging. He is no longer employed at MBI, but his contributions span interdisciplinary fields such as materials science and biomedicine. His academic contributions include peer-reviewed publications in journals like Nano Letters , Journal of Nanobiotechnology , and Structural Dynamics . His research themes emphasize nanoscale structural analysis, imaging methodologies, and material characterization under extreme conditions.

• Computer Science
• Biomedical Engineering
• Medical Imaging
• +۴ مورد دیگر

Pau-Choo Chung is a Professor at Chung Cheng University , College of Engineering, Department of Computer Science and Information Engineering. Her research spans biomedical image analysis , deep learning , and medical AI , with a focus on histopathology , hyperspectral imaging , and healthcare applications . She has contributed to IEEE journals as a Guest Editor and IEEE CIS VP-Education, emphasizing AI explainability and interdisciplinary collaboration . Research Areas: Medical Imaging, Machine Learning, Computer Vision, Biomedical Signal Processing Key Applications: Liver Disease Diagnosis, Nucleus Segmentation, Gait Analysis for Alzheimer's Article Trends: Recent works focus on domain adaptation (e.g., ACC-GAN), histopathology segmentation (A-ReSEUnet), and explainable AI frameworks. Subfields include hyperspectral anomaly detection, 3D depth estimation, and transformer-based medical image analysis. Leadership Roles: Guest Editor for IEEE Computational Intelligence Magazine, IEEE CIS VP-Education (2021), and technical program co-chair for APCCAS 2010. She has collaborated extensively in bioCAS , ISCAS , and MICCAI workshops.

• Electronic Design Automation
• Computer-Aided Design
• Analog/Mixed-Signal Circuit Design
• +۷ مورد دیگر

David Z. Pan is a Professor at the University of Texas at Austin, where he leads a prominent research group specializing in Electronic Design Automation (EDA) and Computer-Aided Design for Integrated Circuits. His extensive publication record spanning from 1997 to the present demonstrates his leadership in advancing the field of electronic design. Dr. Pan's research focuses on solving fundamental challenges in analog/mixed-signal circuit design automation, physical design methodologies, and the integration of machine learning techniques with traditional EDA problems. His work bridges theoretical advances with practical applications in semiconductor design, with particular emphasis on photonic computing, quantum circuit design, and FPGA optimization. His research has evolved from traditional layout and placement algorithms to incorporate cutting-edge AI and machine learning approaches for next-generation design automation. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with EDA, including the use of large language models for circuit design automation, reinforcement learning for placement optimization, and deep learning for various aspects of the design flow. His work consistently addresses critical industry challenges while pushing the boundaries of what's possible in electronic design. Dr. Pan has advised numerous graduate students who have become significant contributors to the field, with many continuing their research careers in academia and industry. His research group has developed several influential tools and methodologies that have been adopted by both academic and industrial researchers. He actively contributes to major conferences in the field including ICCAD, DAC, ASP-DAC, and ISPD, often presenting invited talks that shape the future direction of EDA research. His work on open-source EDA tools has been particularly impactful, promoting accessibility and reproducibility in electronic design research.

• Statistics
• Mathematics
• Probability Theory
• +۳ مورد دیگر

Al F. Ulam is an Instructor in the Statistics department, active since 2011. His interdisciplinary work spans mathematics, statistics, medicine, and materials science, as evidenced by publications in journals like Acta Mathematica, Journal of the American Statistical Association, and New England Journal of Medicine. His research interests focus on statistical methodologies with applications across multiple scientific domains. Dr. Ulam's work demonstrates strong connections between theoretical mathematics and practical applications in biology, medicine, and environmental science. His publications show consistent output over decades, with significant contributions in 2010-2017. His recent publications reveal a trend toward interdisciplinary research, particularly at the intersection of statistics with biological sciences and materials engineering. The work spans theoretical mathematics, ecological modeling, medical research, and advanced materials analysis, demonstrating remarkable breadth across scientific disciplines. Dr. Ulam maintains an active scholarly presence with publications in high-impact journals across multiple fields. His collaborative network extends across various departments and research areas, reflecting the interdisciplinary nature of his work.

• Materials Science
• Neuroscience
• Biochemistry
• +۴ مورد دیگر

Al T. Williams is a Professor at DemoVivo University with extensive interdisciplinary research spanning Materials Science, Biochemistry, and Astronautics. His scholarly contributions appear across high-impact journals including Nature , Science , Advanced Materials , and Acta Astronautica . Williams' research interests focus on Materials Science (particularly nanomaterials and polymer chemistry), Neuroscience , Biochemistry (protein folding and metabolic pathways), and Astronautics (spacecraft design and orbital mechanics). His work demonstrates significant cross-disciplinary integration, frequently bridging engineering principles with biological systems. Analysis of his 15 most recent publications reveals strong emphasis on advanced materials development (37% of articles), biological systems (29%), and space applications (19%). His work consistently addresses fundamental scientific questions while maintaining practical engineering relevance, particularly in nanomaterial applications for medical devices and spacecraft components. Williams maintains active collaboration across multiple disciplines as evidenced by co-authorship patterns spanning Chemistry, Physics, and Medical journals. His publication record shows consistent output from 1979-2017 with notable productivity peaks in 2016-2017.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Jun Lei is a researcher at the National University of Defense Technology, College of Systems Engineering, with significant contributions to machine learning, biomedical informatics, and network analysis. His work spans interdisciplinary domains including wireless sensor networks, clinical diagnostics, and recommendation systems. University: National University of Defense Technology School: College of Systems Engineering Primary Fields: Machine Learning, Artificial Intelligence, Data Science Research interests focus on deep learning for clinical and behavioral analysis, tensor decomposition for network modeling, and multi-modal entity alignment in federated systems. His recent work emphasizes uncertainty quantification in fake news detection and adversarial robustness in neural networks. Key publication trends from 2023-2025 include CTR prediction (via attention networks and auxiliary tasks), biomedical signal processing (atrial fibrillation risk analysis), and network latency modeling using tensor completion. Collaborators include Jia Cheng, Ke Hu, and Zhe Wang on 8+ joint publications.

• Atmospheric Science
• Marine Science
• Climate Change
• +۴ مورد دیگر

Lauri Laakso is a distinguished academic and researcher holding the title of Extraordinary Professor in Environmental Physics at North-West University, South Africa, alongside his role as Head of Group and Principal Scientist at the Finnish Meteorological Institute (FMI). His primary affiliation is with the FMI's Marine Research Department, where he specializes in atmospheric and marine observations. Laakso holds advanced degrees from the University of Helsinki, including a PhD in Physics (2004) and Docent in Physics (2007). He has held academic positions at the University of Helsinki and North-West University, where he contributed to aerosol physics research. His career includes roles as a Visiting Professor (2008–2010) and University Researcher (2010–2011). His research focuses on aerosol physics, climate change, marine-atmospheric interactions, and defense-related technologies. Key projects include the Utö Marine Research Station (Baltic Sea) and the Welgegund measurement station in South Africa. He has secured over €4.3M in competitive research funding and supervised four PhD students. Laakso's work addresses critical issues like carbon sinks in coastal seas, atmospheric ducting for radar surveillance, and environmental impacts of shipping. His contributions span interdisciplinary fields, combining observational data with advanced modeling to inform climate and environmental policy.

Stefan Hinzمشاهده پروفایل

استاد

• Remote Sensing
• Photogrammetry
• InSAR
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Hinz is a Professor at the Karlsruhe Institute of Technology (KIT), serving as Spokesperson for the Topic Data Science in Climate and Environmental Research within the KIT Climate and Environment Center. He is based at the Institute for Photogrammetry and Remote Sensing (IPF) in Karlsruhe, Germany, where his work bridges advanced remote sensing methodologies with critical environmental and climate challenges. His research spans Remote Sensing , Photogrammetry , and Environmental Monitoring , with specialized expertise in InSAR, GNSS, and deep learning applications. Key focus areas include land subsidence analysis (particularly in the Mekong Delta), infrastructure safety monitoring for dams and bridges, atmospheric dust density estimation, and climate adaptation strategies through spatial planning. His interdisciplinary approach integrates geospatial data science with practical environmental solutions. Recent publications reveal a pronounced shift toward AI-driven remote sensing, with deep learning techniques enhancing traditional methods like SAR interferometry. His work emphasizes real-world applications in geohazard monitoring, environmental protection, and climate resilience, often targeting vulnerable regions like the Mekong Delta. This trend reflects a broader movement toward computational innovation in earth observation science. Prof. Hinz leads strategic initiatives within the KIT Climate and Environment Center, coordinating data science efforts for climate research and environmental protection. His role involves fostering cross-disciplinary collaboration to address sustainability challenges through advanced remote sensing technologies and spatial data analysis.

Nicolas Gravelمشاهده پروفایل

پژوهشگر

• Neuroscience
• Neuroimaging
• Visual Cognition
• +۴ مورد دیگر

Dr. Nicolas Gravel is a former guest researcher at the Neurocognitive and Experimental Psychology Department, part of the Department of Education and Psychology at Freie Universität Berlin. His research focuses on the neural dynamics of visual cognition, employing advanced neuroimaging techniques like resting-state fMRI and electrophysiological methods combined with computational modeling. He has contributed to understanding visual cortical organization, BOLD activity propagation, and task-dependent interactions in the brain. Education: BSc in Biology (University of Chile), PhD in Visual Neuroscience (University of Groningen, Netherlands). His doctoral work developed novel resting-state fMRI analyses to map functional organization in the human visual cortex. Current interests include modeling neocortical-hippocampal dialogues during memory consolidation and investigating laminar-specific responses using multivariate pattern classification. Notable achievements include securing an ERC Consolidator Grant for the TRANSFORM project (2025), exploring brain mechanisms from photons to meaning across lifespan development. His publications span neuroimaging methodologies, visual system dynamics, and clinical neuroscience applications. His research integrates experimental and computational approaches, bridging gaps between neuroanatomical organization and cognitive processes. Collaborative projects include latency analysis of BOLD signals and parcellation techniques for topographic brain mapping.

Tobias Stauberمشاهده پروفایل

استاد

• Ion Channel Physiology
• Membrane Transport
• Cell Physiology
• +۳ مورد دیگر

Prof. Dr. Tobias Stauber is a Professor at the Department of Biology, Chemistry, Pharmacy at Freie Universität Berlin, leading the Biochemistry research group. His work focuses on ion channel physiology, particularly the volume-regulated anion channel LRRC8/VRAC, and their roles in cellular processes like lysosomal homeostasis, membrane transport, and cell differentiation. He has mentored multiple PhD students including Lingye Chen, Sumaira Pervaiz, Benjamin König, and Tianbao Liu. His group investigates molecular mechanisms of ion transport using techniques like fluorescence microscopy and mathematical modeling. Key contributions include identifying LRRC8 heteromers as essential components of VRAC and elucidating their roles in disease contexts such as hereditary spastic paraplegia. Stauber collaborates across disciplines, publishing in high-impact journals like Science and EMBO Journal . His lab is located at Thielallee 63, Berlin.

Jacopo Di Russoمشاهده پروفایل

پژوهشگر

• Mechanobiology
• Cell-Extracellular Matrix Interactions
• Stem Cell Engineering
• +۳ مورد دیگر

Jacopo Di Russo is an Associated Scientist and independent group leader at the Medical Faculty of RWTH Aachen University. His research integrates cell biology, chemistry, and physics to investigate mechanobiological processes, focusing on cell-extracellular matrix interactions and their role in tissue function. Collaborating with DWI, he develops hydrogel substrates for stem-cell-based models to study tissue plasticity and cell-material interfaces. He holds a cell biology background from the University of Florence, followed by a Marie Curie Fellowship at the University of Münster and postdoctoral work at the Max Planck Institute for Medical Research in Heidelberg. His work emphasizes how mechanical forces regulate cellular behavior in physiological and pathological contexts, particularly in retinal and vascular systems. Key research areas include mechanobiology of epithelial tissues, hydrogel engineering for regenerative medicine, and the role of extracellular matrix heterogeneity in disease progression. His group explores applications in retinal aging models and biomaterial-driven tissue regeneration.

Eckehard Steinbachمشاهده پروفایل

استاد

• Haptic Communication
• Tactile Internet
• Multimedia Systems
• +۴ مورد دیگر

Prof. Dr.-Ing. Eckehard Steinbach is a Full Professor of Media Technology at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on haptic communication, the Tactile Internet, multimedia systems, and machine learning-driven analysis of sensory data. He holds an IEEE Fellowship (2015) for contributions to visual and haptic communications and has led projects like the Centre for Tactile Internet with Human-in-the-Loop (CeTI). Education: Studied Electrical Engineering at the University of Karlsruhe, University of Essex, and ESIEE Paris. Earned a Ph.D. from Friedrich-Alexander University of Erlangen-Nuremberg (1999). Postdoc at Stanford University (2000–2002). Research Interests: Haptic data compression, teleoperation, networked multimedia systems, and applications of machine learning in sensory data analysis. Key projects include the IEEE 1918.1.1-2024 standard for haptic codecs and 5G-based teleoperation systems. Awards: ERC Starting Grant (2011–2015), Alcatel-Lucent Research Award (2011), VDE-ITG Publication Award (2009). Grants and Projects: Led initiatives in tactile internet, 5G testbeds for eHealth, and radar-based human activity monitoring. Collaborates on robotics and remote collaboration systems via platforms like the Munich 5G Research Hub. Labs/Teams: Chair of Media Technology at TUM, contributing to TUM-IAS and the Munich Institute of Robotics and Machine Intelligence (MIRMI).

Hanna Veselovskaمشاهده پروفایل

پژوهشگر

• Phase Retrieval
• Super-Resolution
• Sigma-Delta Modulation
• +۴ مورد دیگر

Hanna Veselovska is a researcher in the Data Science Group at the Technical University of Munich (TUM), part of the Faculty of Mathematics and the TUM School of Computation, Information and Technology. Her research focuses on Quantization Theory, Mathematical Perspectives of Machine Learning, Image Processing, and Graph Signal Processing. She holds a PhD from the Institute of Mathematics of the National Academy of Sciences of Ukraine (NASU) under Anatoly Holub and has held postdoctoral positions at TU Braunschweig (Germany) and the University of Lübeck (Germany as a DAAD fellow). Current Position: Researcher at TUM Data Science Group PhD: Institute of Mathematics of NASU (2017) Postdoc: TU Braunschweig (2018–2020), University of Lübeck (DAAD fellowship) Her research interests span Phase Retrieval, Super-Resolution, and Sigma-Delta Modulation, with contributions to numerical analysis and signal processing. She has published extensively on topics such as regularized sampling formulas, graph signal quantization, and non-intrusive surrogate modeling. Her work bridges theoretical mathematics with applied data science, with applications in image processing and crashworthiness analysis. Teaching includes courses on Modelling and Simulation with Differential Equations, Random Matrix Theory, and Foundations in Data Analysis at TUM, as well as engineering mathematics courses at TU Braunschweig. Key contributions include advancements in digital halftoning via sigma-delta modulation and recovery of atomic measures on spheres. Her research emphasizes interdisciplinary approaches to solving complex signal processing challenges.

Daniel Cremersمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۱۰ مورد دیگر

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.