جست‌وجوی استاد و پژوهشگر

• Computer Graphics
• Artificial Intelligence
• Virtual Reality
• +۳ مورد دیگر

Tomasz Bednarz is a Professor at the University of New South Wales' School of Engineering and Information Technology, with affiliations at CSIRO Data61 and Kyushu University (PhD). His work bridges Artificial Intelligence , Virtual Reality , and Human-Computer Interaction , focusing on data visualization and creative computation . Key collaborations include researchers from biomedical imaging to urban analytics. University of New South Wales, Sydney, NSW CSIRO Data61, Sydney, NSW Kyushu University, Japan (PhD) His research explores immersive analytics through projects like VR-based emotion elicitation, physics-informed AI for simulations, and AI-enhanced visual storytelling frameworks. Recent work applies Generative AI to graphics and reinforcement learning for location recommendation systems. Publications highlight interdisciplinary impact across conservation , healthcare , and disaster management . Article trends show: (1) 2025 focuses on AI-driven graphics and location recommendation systems (2) 2023-2024 emphasizes VR analytics, wildfire modeling, and AI-human collaboration (3) 2018-2022 expands visualization narratives, eye-tracking, and emotion analysis (4) pre-2015 foundational work in heterogeneous computing and VR tele-operation. Major contributions include: Co-developing Gravity++ for interactive data storytelling Advancing VR for conservation and mining robotics Pioneering touchless biofilm interaction systems Establishing frameworks for 3D urban analytics and medical imaging

• Medical Imaging
• Ultrasound Tomography
• Image Registration
• +۴ مورد دیگر

Torsten Hopp is a Research Associate at the Institute for Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT). His work focuses on advanced medical imaging technologies, particularly 3D ultrasound computer tomography (USCT) and its integration with MRI and X-ray modalities for breast cancer diagnosis. Research Interests: Ultrasound tomography system development Image registration algorithms Machine learning applications in medical imaging Biomechanical and computational modeling GPU-accelerated image reconstruction Multimodal imaging for clinical workflows Publication Trends: Torsten Hopp has contributed extensively to the evolution of 3D USCT, with recent work emphasizing deep learning integration , refraction correction , and patient-specific modeling . His research bridges hardware innovation (e.g., PtQube device) with computational methods for clinical translation. Labs & Teams: Affiliated with KIT's Institute for Data Processing and Electronics , he collaborates on projects like the High Data Rate Processing and Analysis Initiative , advancing ultrasound tomography for breast cancer screening.

• Artificial Intelligence
• Business Informatics
• Semantic Technologies
• +۶ مورد دیگر

Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .

Robert Strzodkaمشاهده پروفایل

استاد

• Parallel algorithms on parallel hardware (GPUs, many-core CPUs, FPGAs)
• Numerical methods (AMG, Krylov, preconditioners, FMM, multilevel methods, SVD)
• Graph algorithms (partitioning, coloring, decomposition, BFS, MST)
• +۷ مورد دیگر

Robert Strzodka is a full professor at Heidelberg University, holding the chair on Application Specific Computing since 2015. He is renowned for pioneering work in GPU-based scientific computing, focusing on optimizing interactions between mathematics, algorithms, and parallel hardware architectures. Current affiliation: Institute of Computer Engineering (ZITI), Heidelberg University Prior roles: NVIDIA (AmgX group), Max Planck Institute (Integrative Scientific Computing), Stanford University (visiting assistant professor) PhD: Numerical mathematics from University of Duisburg-Essen (2004) His research spans parallel algorithms (GPUs, FPGAs), numerical methods (AMG, Krylov, preconditioners), and graph algorithms (partitioning, MST). Publications emphasize GPU acceleration , cache optimization , and mixed precision computing across domains like astrophysics, fluid dynamics, and finite element methods. The 15 most recent articles (2024-2008) demonstrate consistent innovation in parallel computing , GPU algorithms , and numerical methods . Key subfields include stochastic modeling , tridiagonal solvers , level set visualization , and cache-aware algorithms .

Richard Membarthمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Domain-Specific Compilers
• GPU Acceleration
• +۴ مورد دیگر

Richard Membarth is an academic researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Computer Science. His work focuses on high-performance computing, domain-specific compilers, and GPU acceleration. He has contributed to projects like FLOWER (dataflow compiler), Hipacc (image processing DSL), and XEngine (neural network optimization). Membarth's research bridges compiler design, parallel algorithms, and heterogeneous hardware, with applications in medical imaging, bioinformatics, and autonomous systems. Co-developer of AnyDSL framework for partial evaluation Lead in GPU acceleration for molecular dynamics (tinyMD) Specialized in compiler techniques for FPGAs and GPUs Key areas include: - Domain-specific languages (DSLs) - Parallel algorithm optimization - Medical computing pipelines - Real-time graphics rendering

• Neuromorphic Computing
• Reservoir Computing
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Abigail Morrison is a Professor and Group Leader of the Computation in Neural Circuits group at the Institute for Advanced Simulation (IAS-6), part of Forschungszentrum Jülich. Her work focuses on advancing neuromorphic computing, reservoir computing, and spiking neural network simulations. She leads development of the NEST simulator and NESTML modeling language, tools critical for large-scale brain modeling. Key research areas include: Neuromorphic hardware-software co-design Biophysically plausible neural network models GPU-accelerated simulation frameworks Multi-scale brain co-simulation techniques Applications in embodied AI and neuroscience Her lab contributes to the European EBRAINS infrastructure and has pioneered methods for parallel GPU-based network construction. Recent work explores topographic neural circuit architectures for signal processing and reinforcement learning in neuromorphic systems. Notable achievements include: Development of NESTML (v8.0+) ODE-toolbox for ODE solver selection Co-design of HNC neuromorphic compute nodes Advances in dendritic computation modeling Current efforts emphasize exascale computing readiness, neuromorphic system validation, and bridging biological plausibility with computational efficiency in neural network simulations.

Jongse Parkمشاهده پروفایل

دانشیار

• AI Serving Systems
• On-Device AI
• Processing-in-Memory (PIM)
• +۴ مورد دیگر

Jongse Park is currently an Associate Professor at the School of Computing (SoC), KAIST , and a core member of the Computer Architecture and Systems Laboratory (CASYS) . He holds co-affiliations with the School of Electrical Engineering , Graduate School of AI Semiconductor , Graduate School of System Architect , and Department of Semiconductor System Engineering at KAIST. Since 2025, he has been serving as a Visiting Associate Professor at Stanford University's Pervasive Parallelism Lab within the EECS department. PhD in Computer Science, Georgia Institute of Technology (2018), advised by Prof. Hadi Esmaeilzadeh MS in Computer Science, KAIST (2012), advised by Prof. Seungryoul Maeng BS in Computer Science and Engineering, Sogang University (2010) His research focuses on accelerating AI serving systems , enabling on-device AI , processing-in-memory (PIM) architectures , and flexible AI compiler frameworks . Recent work explores transformer optimization, heterogeneous AI semiconductors, and efficient video-language processing. Recent publications include MICRO 2025 work on PIM for LLMs, VLDB 2025 research on video-language engines, and ISCA 2025 contributions to LLM quantization. His team's projects have received funding from the K-Cloud Project , NRF Young Researcher Program , and IITP Core Technology Development grants . Teaching Innovation Award Excellence Prize, KAIST (2025) Samsung Humantech Paper Award Gold Prize (2025) IEEE Senior Member (2024) Best Paper & Distinguished Artifact Awards at IISWC (2024) and ISCA (2024) He actively supervises PhD and MS students in AI systems research and serves on program committees for top conferences like ASPLOS , ISCA , and MICRO , including organizing roles as Sponsorship Chair for MICRO 2025.

• Computer Science Education
• Programming Education
• Computer Architecture
• +۴ مورد دیگر

Sohum Sohoni is a Professor in the School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering at Arizona State University. His research spans computer science education, programming pedagogy, computer architecture, and image quality assessment with GPU acceleration. He has developed innovative educational approaches for computer architecture courses using the PLP instruction set architecture and has contributed significantly to understanding programming education through analysis of error messages and embedded questions. His research interests focus on Computer Science Education , particularly programming education techniques, computer architecture pedagogy, and engineering education methods. He has made substantial contributions to understanding how students learn programming concepts, develop effective debugging skills, and comprehend computer architecture principles. His work bridges theoretical computer science concepts with practical educational applications, emphasizing hands-on learning experiences and innovative assessment methods. His publication trends reveal a strong focus on computer science education research with particular emphasis on programming education (2015-2018), computer architecture education using PLP (2014-2017), and image quality assessment with GPU acceleration (2012-2018). His work consistently combines theoretical computer science concepts with practical educational applications, demonstrating his commitment to improving how computing concepts are taught and learned. His advising work includes mentoring students like Christopher Mar, Harsha B. M. Kadekar, Shaowen Lu, Thien D. Phan, and Vignesh Kannan, who have co-authored publications with him across various computing education topics. His research has been supported through various educational technology projects focused on online learning environments, software engineering education, and computer architecture instruction. He has been actively involved in developing virtualized learning environments for IoT education, creating innovative approaches for software engineering education, and designing effective methods for teaching computer architecture concepts. His work demonstrates a consistent commitment to improving computing education through evidence-based pedagogical approaches and technological innovation.

• High-Performance Computing
• Parallel Computing
• Deep Learning
• +۳ مورد دیگر

Enrique S. Quintana-Ortí is a Professor at the Technical University of Valencia and Jaume I University , Spain, specializing in Computer Science of Systems and Computers . His work bridges High-Performance Computing (HPC) , Parallel Computing , and Deep Learning , with a focus on optimizing Matrix Algorithms for modern architectures. Key research areas: Quantized Inference , GEMM-Based Convolutions , GPU Acceleration , and Performance Portability across ARM, RISC-V, and NVIDIA processors. Recent projects include RED-SEA (European interconnect solutions), GreenLightningAI (decoupled AI systems), and Ginkgo (GPU-based linear algebra frameworks). His publications (2023–2025) emphasize edge computing , mixed-precision techniques , and energy-efficient AI . Collaborative efforts span institutions like Xilinx , Fujitsu , and co-authors such as Adrián Castelló , Héctor Martínez , and Francisco D. Igual .

Ali Aghaeifarمشاهده پروفایل

پژوهشگر ارشد

• Magnetic Resonance Imaging
• High-field MRI
• B0 Field Shimming
• +۵ مورد دیگر

Dr. Ali Aghaeifar is a Postdoctoral Researcher at the Department for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics in Tübingen, Germany. His research focuses on developing advanced magnetic resonance imaging techniques, particularly for ultra-high field (9.4T) brain imaging systems. His research interests span multiple domains of MRI physics and engineering, with emphasis on B0 field shimming , motion correction , and computational simulation of MR phenomena. Aghaeifar has developed several open-source software frameworks including SpinWalk for spin dynamics simulation, TurboBloch for Bloch equation solving, and AMoCo for motion correction in MRI. His recent publications demonstrate expertise in optimizing multi-coil shimming systems, with significant improvements over conventional spherical harmonic approaches. His work bridges physics, engineering, and neuroscience to solve practical challenges in high-field MRI. As a software developer, he has created multiple computational tools implemented in C++ with GPU acceleration using CUDA, and provides interfaces for MATLAB and Python users. His Biot-Savart simulator enables precise calculation of magnetic fields from arbitrary coil geometries.

• Artificial Intelligence
• Machine Learning
• Space Weather
• +۴ مورد دیگر

Dr. Peter Steinbach leads the Group of Artificial Intelligence at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), focusing on interdisciplinary research spanning computational science, space physics, and machine learning applications. His work integrates advanced computing techniques with domain-specific challenges in healthcare, microscopy, and space weather monitoring. Education & Background: While specific educational details are not provided, his research portfolio indicates advanced expertise in physics, computational methods, and software engineering. His career has been centered at HZDR, contributing to major projects like the CLIJ GPU-accelerated image processing framework and space weather studies using ionospheric wave analysis. Research Interests: Steinbach's work emphasizes practical AI applications, including educational tools for machine learning (e.g., "Teaching Machine Learning in 2020") and medical AI systems. His computational science contributions include optimizing high-performance computing for fluid dynamics and parallel processing. In space physics, he investigates VLF transmitter propagation, plasmaspheric dynamics, and geomagnetic storm impacts using satellite data from DEMETER and ground-based networks. Key Projects: Developed CLIJ, a GPU-accelerated image processing tool integrated with Fiji, revolutionizing microscopy workflows. Advanced understanding of ionospheric processes through studies on Schumann resonances and plasmaspheric hiss effects. Pioneered techniques for lightning detection and Q-burst analysis to map Earth's crust conductivity. Technical Contributions: Steinbach has optimized MPI-based fluid dynamics models for GPU systems and created benchmarking tools like gearshifft for heterogeneous computing platforms. His work bridges theoretical research with practical software solutions for the scientific community.

Hubert Welpمشاهده پروفایل

استاد

• Software Engineering
• Pattern Recognition
• Optical Coherence Tomography
• +۲ مورد دیگر

Hubert Welp serves as Professor of Applied Computer Science at Georg Agricola University of Applied Sciences since 2004, concurrently holding leadership roles as Head of the Information Technology and Digitalization Bachelor Program and Head of the Software Engineering Laboratory. His academic foundation in physics transitions into applied computing through industry experience at IBM and MedScience GmbH. His educational trajectory includes: Physics studies at University of Münster (1984-1988) Diploma research in laser spectroscopy at University of Marburg (1988-1990) Doctoral work on “Laser-induced perturbations in Doppler distributions” at University of Marburg (1991-1994) Research focuses on Software Engineering , Pattern Recognition , and Optical Coherence Tomography (OCT) , with significant contributions to biomedical imaging (brain tumor analysis, peripheral nerve visualization) and industrial applications (RFID systems, non-destructive testing). His work integrates applied pattern recognition with spectroscopic OCT for tissue differentiation and material analysis, evidenced by 30+ publications since 2010. As member of both German Physical Society and German Computer Science Society, he bridges theoretical physics with practical computing solutions. Publication analysis (2013-2019) reveals three dominant research vectors: (1) Advanced OCT techniques for medical diagnostics, particularly brain tissue classification using texture features; (2) Industrial adaptation of OCT for coating analysis in conservation science; (3) Algorithmic innovations in spectroscopic data processing. Collaborative work with Martin Hofmann’s group demonstrates consistent output in optics journals and SPIE proceedings, with recent emphasis on GPU-accelerated analysis and ex vivo tumor detection. Directing the Software Engineering Laboratory, Welp oversees development of OCT data processing frameworks and RFID applications. While specific grants aren’t detailed, sustained publication output indicates active research funding cycles. His teaching portfolio in programming, data networks, and applied pattern recognition directly informs laboratory research directions.

Jan Modersitzkiمشاهده پروفایل

استاد

• Image Registration
• Variational Methods
• Medical Imaging
• +۲ مورد دیگر

Jan Modersitzki is a Full Professor of Applied Mathematics at the University of Lübeck, where he leads research at the Institute of Mathematics and Image Computing. He holds a secondary affiliation with Fraunhofer MEVIS, focusing on image registration projects. His academic journey includes a Diploma (1990) and PhD (1995) from the University of Hamburg, followed by a Habilitation (2003) from the University of Lübeck. Career milestones include positions at Emory University, McMaster University, and CAU Kiel. Modersitzki's research centers on computational methods for medical imaging , with emphasis on variational techniques, optimization, and deformable registration. He developed the widely used FAIR toolbox (Flexible Algorithms for Image Registration) and has contributed to foundational theories in image alignment. His work bridges mathematical rigor with clinical applications in oncology, ophthalmology, and pulmonology. Analysis of his 15 most recent publications (2017–2025) reveals dominant themes: multiscale registration , discontinuous deformation modeling , and high-performance computing solutions for medical images. Trends include GPU acceleration, inverse problem optimization, and integration of machine learning with traditional variational frameworks. He actively contributes to academic societies including GAMM, MICCAI, and SIAM. No awards, grants, or supervised students are detailed in available materials.

Rüdiger Westermannمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• Numerical Simulation
• +۷ مورد دیگر

Prof. Rüdiger Westermann is a full Professor leading the Chair of Computer Graphics and Visualization at the Technische Universität München (TUM). His academic career includes roles at RWTH Aachen University (2001–2003) and research stays at Caltech and the University of Utah. He holds a doctorate from the University of Dortmund (1996) and has conducted foundational work in practical computer science, focusing on computer graphics, scientific visualization, and real-time numerical simulation. His research emphasizes algorithm development for interactive data exploration and physical simulation, particularly leveraging many-core architectures. Key research areas include volume visualization, multi-scale finite element simulation, and hierarchical data representation. Recent contributions involve stress-guided 3D design optimization, Bayesian imaging techniques, and GPU-accelerated visualization tools. His work bridges theoretical advancements with practical applications in meteorology, materials science, and medical visualization. Prof. Westermann’s academic journey includes a postdoctoral position under Prof. T. Ertl at Erlangen-Nuremberg (1998–2001) and prior research at GMD St. Augustin (1992–1997). He is actively involved in developing visualization tools for ensemble weather forecasts (e.g., Met. 3D) and structural design optimization for additive manufacturing. His publications span high-impact venues like IEEE Transactions on Visualization and Computer Graphics, emphasizing real-world applicability of visualization and simulation techniques. His research group at TUM collaborates on projects such as the Alpine benchmark for PDE emulators and GPU-based algorithms for large-scale data processing. Current efforts focus on neural fields for ensemble visualization, adaptive sampling techniques, and Bayesian methods for radio interferometry imaging.

Jajnabalkya Guhathakurtaمشاهده پروفایل

پژوهشگر

• Computational Imaging
• Materials Science
• Structural Engineering
• +۳ مورد دیگر

Jajnabalkya Guhathakurta is a Researcher at the Institute of Computer Architecture and Computer Engineering within Faculty 05 of the University of Stuttgart. His work focuses on advanced materials science, computational imaging, and structural analysis. He specializes in composite materials, computed tomography (CT), and the mechanical behavior of advanced composites under various conditions. His research interests include fiber-reinforced concrete systems, interpenetrated composites, and the application of CT imaging for non-destructive evaluation of materials. He has contributed to studies on steel fiber orientation effects, Li-ion cell expansion phenomena, and data-driven material characterization. His work integrates computational methods with experimental analysis, particularly in high-resolution imaging and dynamic material response modeling. Key areas of exploration include the potato effect in Li-ion cells, bubble dynamics in chemical flows, and the optimization of composite material properties through advanced fabrication techniques. His publications span interdisciplinary fields, combining mechanical engineering, materials science, and computational methodologies.