جست‌وجوی استاد و پژوهشگر

Holger Steebمشاهده پروفایل

استاد

• Continuum Mechanics
• Fluid Mechanics
• Porous Media
• +۴ مورد دیگر

Holger Steeb is a Professor and Institute Director at the Institute of Applied Mechanics (MIB) at the University of Stuttgart, where he also holds the Chair of Continuum Mechanics. His academic journey includes a Diplom in Civil Engineering (1990–1995) and a Dr.-Ing. from the University of Stuttgart (2002). He has held positions at Saarland University, the University of Twente, and Ruhr-University Bochum. His research focuses on continuum mechanics, porous media, fluid dynamics, and fracture mechanics, leveraging computational methods and advanced imaging techniques like X-ray tomography. Steeb’s work integrates experimental and numerical approaches to study multiphase flows, geomechanics, and material behavior. He leads interdisciplinary projects involving collaborators from engineering, geology, and environmental science. Key methodologies include pore-scale modeling, additive manufacturing for micromodels, and fracture mechanics simulations. His contributions span energy storage (CO2 sequestration), glaciology, and material characterization of concretes and asphalt. Recent research emphasizes kinetic interface-sensitive tracers for reservoir characterization and the mechanical behavior of shape-memory polymers. His lab employs high-resolution imaging and machine learning for fracture detection and fluid displacement analysis. Steeb’s publications reflect a balance between theoretical frameworks and applied engineering solutions. Professional roles include leadership in the GeomInt initiative studying geomechanical integrity of host rocks. His work bridges micro-scale phenomena with large-scale environmental and infrastructural challenges, influencing both academic and industrial applications.

Lena Cibulskiمشاهده پروفایل

پژوهشگر

• Visualization
• Visual Analytics
• Human-Centered Design
• +۵ مورد دیگر

Lena Cibulski is a Postdoctoral Researcher at the Institute for Visual and Analytic Computing , University of Rostock , Germany. Her work focuses on the design of visualization tools that empower experts to make data-informed decisions, particularly in engineering and life-science contexts where human expertise must be synthesized with large, complex data. Education PhD in Visualization, 2024 – Technical University of Darmstadt, Germany MSc in Computer Science, 2017 – Otto-von-Guericke University Magdeburg, Germany BSc in Visual Computing, 2016 – Otto-von-Guericke University Magdeburg, Germany Research Interests Lena’s research blends computer science, design, and decision theory . She investigates how interactive visualizations can amplify experiential knowledge, support preference construction, and facilitate multi-attribute choices under conflicting objectives. Key themes include: Multivariate and temporal data visualization Human factors and cognition in visual analytics Real-world, application-driven design studies in engineering and life sciences Parameter-space exploration, feature engineering, and causal analysis Publication Trends Across 2020-2025 her articles cluster around three thrusts: (1) foundational work on Pareto-based decision interfaces (PAVED, COMPO*SED), (2) empirical studies of visualization adoption and usability in manufacturing and engineering, and (3) methodological contributions toward understanding decision problems as a primary goal of visualization design. Recent papers extend these ideas to cell-signaling simulations and sustainable-development education. Scientific Awards 2024 – Best Dissertation in Computer Science, TU Darmstadt 2025 – Honorable Mention, VRVis Visual Computing Award Teaching, Outreach & Collaboration Lena currently teaches master-level courses on visualization, visual analytics, and interactive data analysis at the University of Rostock. She is open to multidisciplinary collaborations involving human factors, methodological aspects of visualization research, and real-world applications. Interested students or partners are encouraged to contact her directly.

Hu Caoمشاهده پروفایل

پژوهشگر

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

Christian Heineمشاهده پروفایل

پژوهشگر

• Data Visualization
• Topology-Based Methods
• Scientific Computing
• +۳ مورد دیگر

Christian Heine is a researcher at the Institute of Computer Science , University of Leipzig. His work focuses on advanced data visualization techniques, particularly those grounded in topological and geometric analysis of scalar fields, ensemble data, and high-dimensional datasets. Key Research Areas: Topological visualization, scalar field analysis, medical imaging, and uncertainty quantification. Methodologies: Bayesian inference, fiber trajectories, volume rendering, and dynamic workflows. Applications: Meteorological data analysis, medical diagnostics, and interactive visualization systems. He has published extensively on these topics, with recent work addressing spatio-temporal trends in climate data and noise-robust visualization techniques. His research often integrates interdisciplinary approaches, bridging computer science and applied sciences.

• Computer Vision
• Multimedia Systems
• Machine Learning Applications
• +۷ مورد دیگر

Timothy K. Shih is an active academic researcher with over 30 years of scholarly contributions, evidenced by his extensive publication record from 1991 through 2025. With more than 380 publications spanning numerous prestigious venues including IEEE Access, Multimedia Tools and Applications, and Lecture Notes in Computer Science, he maintains a robust research profile with consistent annual output (20+ papers in peak years). His work demonstrates leadership through frequent senior/corresponding author positions and collaborations with numerous researchers across international institutions. Dr. Shih's research interests encompass a diverse range of computer science disciplines with particular emphasis on Computer Vision , Human-Computer Interaction , and AI Applications . His work bridges theoretical advancements with practical implementations in educational technology, accessibility solutions, and multimedia systems. Recent publications reveal a strategic focus on applying deep learning techniques to solve real-world problems in sign language recognition, gesture analysis, and wireless sensing applications. Analysis of his publication trends over the past five years shows increasing specialization in multimodal AI systems, with significant contributions to sign language technology (including Arabic Sign Language recognition), WiFi-based human activity recognition, and music technology applications. His research demonstrates strong interdisciplinary connections between computer vision, machine learning, and human-centered computing, with practical applications spanning educational technology, accessibility solutions, and smart environments. Through his mentorship, Dr. Shih has guided numerous junior researchers who have become frequent collaborators, including Chih-Yang Lin, Hsin-Hung Cho, and Tipajin Thaipisutikul. His research program appears well-funded through consistent publication output across multiple project areas, suggesting successful grant acquisition in computer vision, AI, and educational technology domains. Current work indicates active involvement in cutting-edge research on diffusion models for audio processing, enhanced sign language recognition systems, and novel approaches to WiFi-based human interaction analysis.

Simon Christمشاهده پروفایل

پژوهشگر

• Computational Biology
• Biophysics
• Reproducible Science
• +۴ مورد دیگر

Dr. Simon Christ is a Researcher at the Department of Computational Biology within the Institute of Cell Biology and Biophysics at Leibniz University Hannover's Faculty of Natural Sciences. His office is located in Building 4160, Room 110 at Herrenhäuser Str. 2, 30419 Hannover. He can be contacted via phone (+49 511 762 16174) or email (christ@cell.uni-hannover.de). Christ holds a Doctor rerum naturalium (PhD) from the University of Potsdam (2020) and a Master of Science from Humboldt University of Berlin (2014). His doctoral research focused on morphological transitions of vesicles under nonuniform conditions, while his master's thesis examined non-Markovian systems in statistical physics. His research focuses on computational approaches to biological systems, including: Development of reproducible scientific workflows and open-source tools Agent-based simulations of biological processes Numerical solutions of differential equations in biological contexts Computational modeling of membrane dynamics and vesicle behavior Research software engineering practices and tools He is proficient in multiple programming languages including Julia, C/C++, MATLAB, Mathematica, Python, and R, and is a certified instructor for The Carpentries. Christ's recent publications demonstrate a strong focus on research software engineering, computational biology methods, and biophysical modeling of cellular systems. His work frequently appears in computational biology, biophysics, and software engineering journals, with consistent contributions to open science practices and reproducible research methodologies. No specific awards or student advising relationships are mentioned in the available documentation. His current work appears to be conducted within the computational biology research group at Leibniz University Hannover, with collaborations extending to other institutions including the University of Potsdam and Max Planck Institute.

Hao Xingمشاهده پروفایل

پژوهشگر

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +۴ مورد دیگر

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Jong-Hwan Kim is a Professor at KAIST's College of Engineering, specialized in robotics and artificial intelligence. His research focuses on neural networks, autonomous systems, computer vision, and human-robot interaction. He has published extensively in top-tier conferences like CVPR, ICRA, and IEEE Transactions. Key contributions include work on developmental learning networks, episodic memory models, and AI applications in robotics and healthcare. He co-chaired the RiTA conference series and has collaborated with institutions globally. His research bridges theoretical advancements with practical applications in robotics, medical diagnostics, and multimodal AI. Affiliations: KAIST (main), Seoul National University (PhD 1987) Research Highlights: Visual odometry, gesture recognition, emotion modeling, and robotics task intelligence Recent projects include text recognition via finger movement, Alzheimer's disease classification using EEG-FNIRS fusion, and multimodal emotion recognition systems. His work emphasizes interdisciplinary approaches, integrating robotics, computer vision, and cognitive science.

Stefan Gumholdمشاهده پروفایل

استاد

• Computer Graphics
• Visualization
• Medical Imaging
• +۳ مورد دیگر

Professor Stefan Gumhold is a leading academic in computer graphics and visualization at TU Dresden. He has held roles including Head of the Chair for Computer Graphics and Visualization since 2005 and served as Dean of the Faculty of Computer Science from 2010 to 2012. His expertise spans visualization techniques, medical imaging, and immersive technologies. Gumhold earned his Ph.D. in 2000, focusing on mesh compression, and later received a Dissertation Award in the same year. He has contributed to projects like the Heisenberg Scholar Research Group and led the Kommission Umwelt at TU Dresden. His teaching includes courses such as Introduction to Computer Graphics, Data Visualization, and Scientific Visualization. Gumhold’s research emphasizes visualization platforms (e.g., ISAAC), image fusion, and neural network applications in medical diagnostics. His work often integrates virtual reality for data analysis, as seen in tools like VRCellLabeler and ExtremeWeatherVis. Education: Diploma in Computer Science (University of Tübingen, 1998), M.Sc. in Applied Physics (University of Massachusetts Boston, 1996). Research Highlights: Development of visualization frameworks, 3D reconstruction, and invertible neural networks for outlier detection. Awards: Dissertation Award 2000. Grants & Labs: Leadership in TU Dresden’s Computer Graphics lab and contributions to collaborative research projects like Fast-Haptic.

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

Hongbo Liu is a researcher at Indiana University - Purdue University Indianapolis , Department of Computer Information and Graphics. With a focus on Artificial Intelligence, Machine Learning, and Network Analysis , Liu has contributed extensively to computational intelligence through 118+ publications since 2004. Multi-disciplinary research spanning Graph Theory, Swarm Intelligence, and Deep Learning Recent work includes Robust Gated Models for Temporal Networks and Self-Adaptive Neuroevolution Systems (2024-2025) Key research themes include: Dynamic network analysis and link prediction Crowd behavior modeling and trajectory forecasting Swarm-based optimization for complex systems Fuzzy logic and granular computing applications Neural network architectures for image and text processing Liu's publications demonstrate strong collaborations with researchers like Ajith Abraham, Yu Yang, and Bo Zhang across 15+ academic journals and conferences . The work spans from theoretical graph algorithms (2015-2017) to applied systems in autonomous robotics and blockchain (2024).

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +۸ مورد دیگر

Harald Reiterer is a researcher affiliated with the University of Konstanz , Germany. His work spans Human-Computer Interaction (HCI) , Augmented Reality (AR) , Virtual Reality (VR) , and Mobile Health over three decades. Key Research Areas : Cognitive workload measurement, collaborative immersive analytics, spatial memory in AR/VR, hybrid user interfaces, persuasive technology for health behavior change. Scientific Contributions include designing AR/VR systems for collaborative workflows, evaluating ergonomic patient transfers, and developing tools for visualizing spatio-temporal data. His recent work focuses on haptic props for AR, cognitive load assessment via eye tracking, and multimodal feedback in VR cockpits. Publication Trends : 15+ recent papers on AR/VR interaction techniques, mixed reality visualization, and health-focused digital interventions.

• Graph Theory
• Network Analysis
• Machine Learning
• +۳ مورد دیگر

Manuel Curado is a researcher with a focus on graph theory, network analysis, and machine learning applications. His work spans spatio-temporal forecasting, traffic accident prediction, medical imaging, and urban mobility studies. He collaborates extensively with researchers like José-Francisco Vicent, Francisco Escolano, and Leandro Tortosa. 2025 : Self-explainable graph convolutional recurrent networks for traffic forecasting 2024 : GeoCC-ConvLSTM for air quality and CTextureFusion for lung CT super-resolution 2023 : Return Random Walk Gravity Centrality for node influence metrics 2022 : Human mobility analysis using Twitter data and directed graph centrality models His research emphasizes graph-based algorithms, deep learning, and data science for real-world problems in transportation and healthcare. He has contributed to journals like Information Sciences , Applied Soft Computing , and Neural Computing & Applications .

• Computer Graphics
• 3D Visualization
• Machine Learning in Graphics
• +۵ مورد دیگر

Sebastian Wohner is a researcher at the Chair of Computer Graphics and Visualization (Prof. Westermann) at the Technical University of Munich. His work focuses on advanced visualization techniques, machine learning applications in graphics, and GPU-accelerated algorithms for 3D design and simulation. He actively contributes to projects such as the NVIDIA CUDA Research Center and ERC-funded initiatives like SaferVis and realFlow, emphasizing real-time liquids and safer visualization systems. His research interests span 3D Gaussian splatting, topology optimization, neural fields for statistical dependencies, and spatio-temporal flow visualization. He has pioneered methods for compressing meteorological ensembles and accelerating novel view synthesis in consumer devices. Wohner also explores GPU-based linear algebra optimizations and efficient rendering techniques for ribbons and twisted lines. In teaching, he leads courses on game physics, visual data analytics, deep learning in computer graphics, and topology optimization. Notable contributions include the development of the Particle Engine and Bunny Demo applications, as well as advancements in differentiable rendering and robotic perception systems. His work bridges theoretical foundations with practical implementations in both academia and industry.

• Computer Graphics
• Visualization
• 3D Reconstruction
• +۳ مورد دیگر

Susanne Weitz is a Researcher at the Chair of Computer Graphics and Visualization, Technical University of Munich, working under Prof. Westermann. Her office is located in room 02.13.052 at Boltzmannstr. 3, 85748 Garching b. München, with contact email susanne.weitz@tum.de and office hours Monday–Thursday 9:00–15:00. Her research focuses on advancing computer graphics through 3D Gaussian Splatting, topology optimization, and neural rendering techniques. Key interests include efficient novel view synthesis for consumer devices, scientific visualization of meteorological data, and geometry processing for cinematic applications. She bridges theoretical innovation with practical implementation, emphasizing real-time performance and accessibility. Analysis of her 2022–2025 publications reveals a strong trajectory in deep learning-enhanced graphics, particularly for 3D reconstruction and visualization. Work spans Gaussian Splatting optimizations, turbulence analysis in fluid dynamics, and lattice structure design, demonstrating interdisciplinary applications from medical visualization to climate science. At TUM, she teaches core courses including Game Physics, Visual Data Analytics, Deep Learning in Computer Graphics, and Machine Learning for 3D Geometry across winter and summer terms. Her curriculum integrates cutting-edge research like neural rendering and topology optimization into practical student projects.

Sonja Grünمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +۳ مورد دیگر

Prof. Sonja Grün is a Professor and Director of the Computational and Systems Neuroscience division (IAS-6) and the JARA-Institute Brain structure-function relationships (INM-10) at Forschungszentrum Jülich. She leads the Statistical Neuroscience research group, focusing on cell assemblies, dynamical neuronal interactions in cortex, higher-order correlation analysis, and reproducible workflows. Her work bridges experimental and computational approaches to understand neural coding and network dynamics. Key contributions include methodologies for spike pattern detection (e.g., SPADE) and tools for data curation (UnitRefine). She actively develops neuroinformatics standards (e.g., Neuroelectrophysiology Analysis Ontology) and promotes reproducible research through provenance tracking initiatives like NFDI-Neuro. Her research spans motor cortex activity, visual processing, and model validation frameworks. Research interests emphasize spatio-temporal spike patterns, cortical network models, and artifact detection in high-density recordings. She collaborates on projects integrating electrophysiological data with computational simulations (e.g., NEST) and brain wave analysis (Cobrawap). Current efforts address challenges in data sharing, artifact removal, and scalable analysis pipelines for heterogeneous datasets.