جست‌وجوی استاد و پژوهشگر

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.

Nicolai Petkovمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۷ مورد دیگر

Nicolai Petkov is a Professor in the Department of Computing Science at the University of Groningen's Faculty of Science and Engineering. He leads research in computer vision, image processing, and computational neuroscience, with a focus on biologically inspired algorithms. His work bridges theoretical neuroscience and practical computer vision applications. His research interests span computer vision, image processing, pattern recognition, computational neuroscience, and biologically inspired computing. Petkov has made significant contributions to contour detection, texture analysis, and visual perception modeling, developing algorithms inspired by the human visual cortex. His work on B-COSFIRE filters has become influential in the field of biologically inspired computer vision. Petkov's publication record shows consistent research output over decades, with recent work focusing on applications in agriculture, robotics, and medical diagnostics. His research demonstrates a progression from theoretical models of visual perception to practical applications in diverse domains. Best paper award for 'Image-based localisation using Gaussian processes' (2016) Best student paper award for 'Contour detection by multiresolution surround inhibition' (2006) Best student paper award for 'Integrated electronic health record database management system' (2015) Top-1% in engineering according to the Essential Science Indicators of the ISI Web of Knowledge Petkov has supervised numerous PhD students and collaborators including George Azzopardi, Nicola Strisciuglio, and Giovanni Papari. His research has received funding for projects in intelligent systems and computer vision applications. He is actively involved in governance at the University of Groningen, having served on the University Council. His laboratory, part of the Intelligent Systems research group, focuses on developing brain-inspired computing approaches with applications ranging from agricultural technology to medical diagnostics and autonomous robotics.

• Fluid Dynamics
• Quantum Computing in Fluid Mechanics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Josef Winter is a researcher at the Department of Aerodynamics and Fluid Mechanics within the TUM School of Engineering and Design at the Technical University of Munich. His work focuses on advanced numerical methods for fluid dynamics simulations, integrating quantum computing, machine learning, and Bayesian optimization to enhance computational efficiency and accuracy. University: Technical University of Munich School: TUM School of Engineering and Design Department: Department of Aerodynamics and Fluid Mechanics Academic Rank: Researcher Email: josef.winter@tum.de Dr. Winter's research interests include: Quantum algorithms for fluid dynamics Multi-fidelity and Bayesian optimization techniques High-order numerical methods Level-set-based sharp-interface simulations Deep reinforcement learning for flow optimization Complex flow modeling across scales His publication trends indicate a strong focus on merging quantum computing with classical fluid dynamics simulations, developing adaptive solvers like ALPACA, and optimizing numerical schemes for compressible and multiphase flows. Recent works explore dynamic circuits for quantum lattice-Boltzmann methods and multi-objective optimization frameworks. Dr. Winter's articles cover diverse aspects of fluid mechanics, including: Quantum computing applications in fluid simulation Level-set methods for interface dynamics BAYESIAN OPTIMIZATION OF NUMERICAL SCHEMES Multi-fidelity surrogate models for complex flows High-order methods for conservation laws Deep reinforcement learning for dynamic systems

• Geomorphology
• Geomorphometry
• Geoinformation
• +۳ مورد دیگر

Dr. Leszek Gawrysiak is a University Professor at the Department of Geology, Soil Science and Geoinformation, Maria Curie-Skłodowska University, Lublin. His academic journey includes a Master's (1993) and PhD (2003) in Earth Sciences from the same university, followed by habilitation in 2019. Education: MA in Geography (1993), Maria Curie-Skłodowska University PhD in Earth Sciences (2003), Maria Curie-Skłodowska University Habilitation (2019), Maria Curie-Skłodowska University Research Interests focus on Geomorphology , Geomorphometry , and Geoinformation . His work integrates GIS and remote sensing with field studies to analyze terrain evolution, particularly in loess areas and proglacial environments. Scientific Trends from his 15 most recent publications (2015-2024) reveal expertise in loess topography , proglacial valley dynamics , forest cover monitoring , and geomorphological mapping . Methodologies include multiresolution DTMs , LiDAR , and historical sediment analysis . Awards: Award of the Association of Polish Geomorphologists (2002) Rector Team Award, Maria Curie-Skłodowska University (2010) Rector Individual Award, Maria Curie-Skłodowska University (2013) Scientific Activity includes participation in projects like Extreme meteorological events in Poland and Geopark Małopolski Przełom Wisły development . He has contributed to digital geomorphological cartography and Polish-Belarusian-Ukrainian water policy frameworks.

Mario Botschمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Virtual Reality
• +۷ مورد دیگر

Mario Botsch is a Professor in the Department of Computer Science at the Faculty of Technology, University of Bielefeld. His research focuses on computer graphics and geometry processing, with major contributions to polygonal mesh modeling, virtual reality, and GPU-accelerated rendering techniques. Research Interests: Immersive data visualization in 3D knowledge spaces Virtual reality for historical relationship discovery Point-based rendering and surface splatting GPU-accelerated geometric modeling Mesh processing and multiresolution modeling Surface reconstruction and anti-aliasing Scientific Contributions: Developed Phong splatting techniques for high-quality rendering Created multiresolution frameworks using displacement volumes Innovated feature-sensitive sampling for mesh optimization Pioneered GPU-based tolerance volume analysis for error control Established OpenMesh data structure for polygonal mesh handling

• Nonlinear System Identification
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Hua-Liang Wei is a Senior Lecturer at the University of Sheffield 's School of Electrical and Electronic Engineering. He leads two innovative research labs: the Dynamical Modelling, Data Mining and Decision Making (3DM) and the Digital Medicine & Computational Neuroscience (DMCN) Research Groups. Specializes in system identification for nonlinear dynamics Develops interpretable AI for healthcare applications Active in space weather and environmental forecasting His methodological expertise spans NARMAX modeling, wavelet neural networks, and multiresolution analysis. Collaborations include Sheffield Teaching Hospitals NHS Trust, multiple University of Sheffield departments (Chemistry, Oncology, Psychology), and international institutions like Beihang University. Scientific Awards include STFC and NERC grants for radiation belt modeling and environmental systems research, EU Horizon 2020 funding, EPSRC Platform grants, Royal Society support, and medical charity partnerships. Recent publications focus on hybrid wavelet-LSTM for wind power forecasting EEG analysis in epilepsy and Alzheimer's domain adaptation for fault diagnosis interpretable models for medical data covering applications from renewable energy to clinical diagnostics.

Jill Naimanمشاهده پروفایل

عضو هیئت علمی

• Data Visualization
• Scientific Digitization
• Machine Learning
• +۴ مورد دیگر

Jill Naiman is a Teaching Assistant Professor at the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign and a Visiting Scholar at the National Center for Supercomputing Applications (NCSA). She holds a PhD in Astronomy & Astrophysics from the University of California, Santa Cruz, where she studied feedback in star clusters and galaxy formation simulations. Prior to her current role, she completed postdoctoral fellowships at the Harvard-Smithsonian Center for Astrophysics, including the National Science Foundation and Institute of Theory and Computation Fellowships. Her research focuses on data visualization , scientific digitization with machine learning , and image processing , particularly in astrophysics. She develops open-source tools for cinematic and interactive visualization of astrophysical simulations, such as AstroBlend and Ytini, and explores methods to extract insights from historical scientific literature through projects like the Reading Time Machine . Her recent publications highlight expertise in astrophysical data analysis , 3D visualization , and software development using tools like Houdini and Blender. These works span adaptive mesh refinement , scientific graphics , and multiresolution data analysis . NASA grant to digitize astrophysical literature National Science Foundation Postdoctoral Fellowship Institute of Theory and Computation Fellowship She actively mentors students, contributes to educational outreach, and teaches courses in Data Visualization and Data Storytelling at the iSchool, including Fall 2025 offerings in IS389JPN, IS445BCG, IS445BCU, IS457BCG, IS457BCU, and IS589JPN.

Karol Dziedziulمشاهده پروفایل

دانشیار

• Wavelet Analysis
• Besov Spaces
• Riemannian Manifolds
• +۴ مورد دیگر

Karol Dziedziul is an Associate Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. He serves as Head of the Division of Nonlinear Analysis. His primary affiliations center on advanced mathematical research with interdisciplinary applications spanning statistics and biochemistry. His research focuses on: Wavelet analysis and functional decompositions on Riemannian manifolds/spheres Statistical estimation methods, particularly density smoothness estimation Interdisciplinary applications in biochemistry (catechins, DNA methylation) Analyses of his 15 most recent publications reveal three dominant themes: (1) Theoretical work on orthogonal projections and wavelet frames in non-Euclidean spaces (2021-2023), (2) Methodological innovations in statistical estimation (2014-2019), and (3) Biochemical investigations of plant enzymes and antioxidants (2013-2023). He teaches extensively in mathematics and statistics, with 195 documented courses including: Statistics I/II Risk Management SAS Programming Machine Learning Futures & Derivatives He frequently supervises diploma theses in Financial Mathematics.

Xiaoquan William Wenمشاهده پروفایل

استاد

• Bayesian statistics
• Computational statistics
• Probabilistic graphical models
• +۵ مورد دیگر

Xiaoquan (William) Wen is Professor of Biostatistics in the University of Michigan School of Public Health , Department of Biostatistics. Since earning his PhD in Statistics from the University of Chicago in 2011, he has been a continuous member of the Michigan faculty and is an active contributor to the NIH GTEx Consortium and the Center for Statistical Genetics . Education PhD in Statistics, University of Chicago, 2011 Research Focus Wen’s methodological work lies at the intersection of Bayesian statistics , computational genomics , and probabilistic graphical models . He develops scalable algorithms for Bayesian model comparison and false discovery rate control , with particular emphasis on multi-tissue eQTL discovery , causal inference from high-dimensional omics data, and integrative analysis of genome, transcriptome, and proteome to dissect the molecular basis of complex human traits. Applied domains include functional genomics, pharmacogenomics, immunogenomics, and pediatric gene–environment interactions, where he translates statistical innovation into biologically meaningful insight. Publications Snapshot (2022–2025) Recent work spans large-scale GWAS meta-analyses, single-cell multi-omics, metabolome-wide Mendelian randomization, and robust replicability frameworks. Recurring themes are causal gene prioritization, fine-mapping, and rigorous statistical validation across diverse human tissues and disease contexts. Scientific Awards (no awards explicitly listed in provided text) Grant & Team Activity Wen is an ongoing NIH GTEx project investigator and contributes to the Michigan Center for Statistical Genetics . While explicit grant numbers are absent, his sustained publication stream and consortium involvement indicate active federal and collaborative funding. No advisees are named in the supplied material. Laboratory & Software He maintains an active GitHub repository releasing open-source tools such as BLIMP (Best Linear IMPutation), SLAT (gene- and pathway-level testing), and QuASAR (quantitative allele-specific analysis).

Martin Ehlerمشاهده پروفایل

دانشیار

• Computational Harmonic Analysis
• Mathematical Data Analysis
• Image Processing
• +۳ مورد دیگر

Martin Ehler serves as Associate Professor and Vice-Dean for Teaching at the University of Vienna's Faculty of Mathematics, where he concurrently holds the position of Deputy Head of the Institute of Mathematics. His academic activities are centered at Oskar-Morgenstern-Platz 1, Room 10.128, Vienna, with primary responsibilities spanning research leadership, teaching undergraduate/graduate mathematics courses, and administrative oversight of academic programs. His research program in Computational Harmonic Analysis drives innovations in Mathematical Data Analysis and Medical Image Processing , evidenced by his leadership in the Applied Harmonic Analysis Cluster (AHA) and co-organization of the bi-annual Strobl conference. Core methodologies integrate frame theory, manifold approximation, and spherical design principles to address challenges in neural network stability, medical imaging reconstruction, and signal processing robustness. Analysis of his 2023-2025 publications reveals dominant themes in harmonic analysis applications to deep learning architectures (particularly ReLU-layer injectivity and filterbank design), geometric approximation on manifolds, and medical imaging algorithms. These works demonstrate consistent interdisciplinary collaboration across mathematics, signal processing, and biomedical engineering domains. No scientific awards were documented in the provided materials. While teaching responsibilities for courses like Introduction to Analysis and Mathematics of Data Science are explicitly listed, details regarding graduate student supervision or research grant funding were absent from the source texts. His research ecosystem is anchored in the Applied Harmonic Analysis Cluster (AHA), facilitating cross-institutional collaboration on harmonic analysis applications, with documented partnerships through co-authored publications in venues like IEEE Signal Processing Letters and Mathematics of Computation.

Steve Petruzzaمشاهده پروفایل

استادیار

• High-Performance Computing
• Data Analytics
• Scientific Visualization
• +۳ مورد دیگر

Dr. Steve Petruzza is an Assistant Professor in the Department of Computer Science at Utah State University (USU) and a Staff Scientist at the Scientific Computing and Imaging (SCI) Institute's Center for Extreme Data Management, Analysis, and Visualization (CEDMAV). He holds a PhD in Computer Science from the University of Rome 'Tor Vergata' and has conducted research at the University of Utah and KAUST. His work focuses on High-Performance Computing (HPC), scientific visualization, and scalable data analytics with applications in edge computing, cloud optimization, and large-scale scientific data management. Education: PhD in Computer Science, University of Rome Tor Vergata (2018) M.S. in Computer Engineering, University of Rome Tor Vergata (2013) B.E. in Computer Engineering, University of Rome Tor Vergata (2010) Research Interests: Dr. Petruzza’s research spans high-performance computing systems, in situ analytics, parallel I/O optimization, and scalable visualization frameworks. His work addresses challenges in managing and analyzing massive scientific datasets across domains like materials science, climate modeling, and geospatial imaging. Grants & Projects: National Science Foundation (NSF) grant for scalable I/O runtimes and adaptive data layouts. USGS collaboration for real-time multispectral aerial mapping. NSF ASPIRE ERC project for equitable EV charging infrastructure visualization. Leadership in the ALPINE project for exascale visualization infrastructure (DOE ECP). Labs & Teams: He leads the Computing Elevated Lab, which develops cutting-edge techniques for large-scale data analysis and visualization across edge devices to supercomputers. Collaborations include the OpenVisus framework for scientific data management and the ALPINE Ascent in situ visualization infrastructure.

Michael Hertyمشاهده پروفایل

استاد

• Hyperbolic Conservation Laws
• Balance Laws
• Kinetic Theory
• +۵ مورد دیگر

Professor Michael Herty is a faculty member at RWTH Aachen University, where he works in the Department of Geometry and Practical Mathematics. His research spans multiple international collaborations, including guest professorships at Southeast University in Nanjing, China and as an Extraordinary Professor at the University of Pretoria, South Africa. His research focuses on Hyperbolic Conservation and Balance Laws , Kinetic and Meanfield Theory , Inverse Problems and Nonlinear Optimization , and Control and Stabilization . His work bridges theoretical mathematics with practical applications in traffic flow, gas networks, and optimization problems. Professor Herty has developed numerical methods for coupled systems, multi-scale problems, and uncertainty quantification in fluid dynamics. His recent publications (2023-2025) demonstrate a strong trend toward consensus-based optimization methods, mean-field limits, and numerical schemes for hyperbolic systems. These works often involve interdisciplinary collaborations across mathematics, engineering, and computational science. His research shows increasing focus on machine learning integration with traditional numerical methods and applications to real-world network problems. Professor Herty serves on multiple editorial boards including: Communication in Mathematical Sciences Networks and Heterogenous Media Communications in Mathematical Research Communications in Applied and Industrial Mathematics Annali dell'Universita' di Ferrara -- Sezione VII - Scienze Matematiche He has supervised numerous PhD and Master's students, with recent theses focusing on multiresolution grid adaptation, turnpike properties in optimal control, and gradient-free optimization methods. His research is supported by multiple DFG projects, EU Marie Curie Doctoral Networks (DATAHYKING), and SPP projects on multi-scale modeling and control. Professor Herty leads significant collaborative efforts in the fields of hyperbolic balance laws, compressible Euler equations, and traffic flow modeling.

Sergey Litvinovمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Biomedical Engineering
• Industrial Applications
• +۶ مورد دیگر

Sergey Litvinov is a Researcher at ETH Zürich, affiliated with the Department of Structural Mechanics and Monitoring within the College of Civil, Environmental and Geomatic Engineering. His work focuses on computational and statistical models for biomedical and industrial applications, leveraging tools like C++, PyTorch, and cloud computing. He contributes to hierarchical Bayesian analysis on big data and probabilistic programming, emphasizing data-driven methodologies. Current projects include the DCoMEX initiative (Data-Driven Computational Mechanics at Exascale), aiming to advance large-scale computational frameworks for structural mechanics and monitoring. His research interests span computational mechanics, fluid dynamics, and machine learning applications, with a strong emphasis on hybrid numerical methods and high-performance computing for multiphase systems. He has no listed scientific awards or advisees. His contributions are centered on software development (e.g., Aphros and Mirheo ) for multiphase flow simulations and novel optimization techniques for inverse problems, particularly in biomedical and engineering contexts. Recent articles highlight his work on discrete loss optimization, reinforcement learning for flow control, and data-driven methods for tumor growth modeling and drug delivery systems. He is part of interdisciplinary teams addressing challenges in microfluidics, turbulence modeling, and biomedical applications, with a focus on scalable solutions for complex systems. His projects often involve collaboration with industrial and academic partners on computational models for real-world applications.

Emily Foxمشاهده پروفایل

استاد

• Time Series Analysis
• Machine Learning
• Health Informatics
• +۲ مورد دیگر

Emily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University, with affiliations at the Wu Tsai Neurosciences Institute, Wu Tsai Human Performance Alliance, Bio-X, and Center for Artificial Intelligence in Medicine & Imaging (AIMI). Previously, she was Amazon Professor of Machine Learning at the University of Washington and Distinguished Engineer at Apple. Education includes: Ph.D. in Electrical Engineering and Computer Science from MIT (2009), E.E. from MIT (2008), M.Eng. from MIT (2005), and S.B. from MIT (2004). Research focuses on modeling complex time series in health applications, particularly from wearables and neuroimaging. Key areas include Bayesian nonparametrics, time series analysis, and developing statistical methods for high-dimensional data. Significant awards include: IMS Fellow, CZ Biohub Investigator, PECASE Award, Sloan Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Advises doctoral students and postdocs in machine learning and statistics. Research supported by NSF, ONR, Amazon, and other grants. Leads research on health AI applications and develops novel statistical methods for large-scale data analysis.

Laurence Leherteمشاهده پروفایل

پژوهشگر

• Electron Density Analysis
• Molecular Dynamics
• Non-Covalent Interactions
• +۳ مورد دیگر

Laurence Leherte is a researcher at the Unit of theoretical and structural physico-chemistry within the University of Namur , affiliated with multiple research institutes including NAmur MEdicine & Drug Innovation Center and Namur Research Institute for Life Sciences. Their work spans computational chemistry, structural biology, and supramolecular systems. Research Interests : Electron density analysis, molecular dynamics simulations, non-covalent interactions in proteins, theoretical modeling of supramolecular assemblies, and computational drug design. Their projects include analyzing metal complexes in resorcinarene cages and studying biomimetic cavity complexes. Scientific Awards : Recipient of prestigious fellowships such as the NATO research grant (1993), FSR funding (2001), FNRS subvention (2015), and the Classe des Sciences annual chemistry prize (2009). Collaborations : Active in interdisciplinary projects with institutions like the University of Namur, contributing to journals including Inorganic Chemistry Frontiers and Theoretical Chemistry Accounts .