جستجوی استاد راهنمای دانشگاهی

Adam Runionsمشاهده پروفایل

پژوهشگر

• Computational Modeling
• Developmental Biology
• Computer Science
• +۴ مورد دیگر

Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.

Volker J Schmidمشاهده پروفایل

استاد

• Bayesian Statistics
• Spatial Statistics
• Medical Imaging
• +۷ مورد دیگر

Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.

Gaël Kermarrecمشاهده پروفایل

پژوهشگر

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +۳ مورد دیگر

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

H. Siegfried Stiehlمشاهده پروفایل

استاد

• Computer Vision
• Computational Neuroscience of the Visual System
• Cognitive Science
• +۳ مورد دیگر

Prof. Dr.-Ing. H. Siegfried Stiehl is a retired Senior Professor (until Sept 2021) at the Department of Informatics, University of Hamburg. He previously held roles including Dean of the Faculty of Mathematics, Computer Science, and Natural Sciences (2001–2006), Vice President for Research (2007–2013), and Head of the Image Processing Research Group. His academic journey includes a PhD from TU Berlin (1980) and a Habilitation in Computer Vision (1987). Education: 1973: Ing. Degree in Ingenieur-Informatik, Fachhochschule Furtwangen 1976: Diploma in Computer Science, TU Berlin 1980: Dr.-Ing. Dissertation on medical image processing, TU Berlin Research focuses on Computer Vision , Computational Neuroscience , and Cognitive Science , with contributions to medical image registration, 3D landmark detection, and biomechanical modeling. Key projects include the EU-funded 'COVIRA' consortium (1989–1995) and leadership in the SFB 950 'Manuscript Cultures' project (2015–2019). His 110+ publications span biomedical image registration, elastic deformation algorithms, and real-time signal processing. Notable collaborations include work with institutions like the University of Pennsylvania, University of Birmingham, and Philips Research. Leadership roles include organizing scientific events, serving on editorial boards (e.g., Biological Cybernetics), and founding the Interdisciplinary Nanoscience Center Hamburg (INCH) in 2001. His research has addressed challenges in neurosurgical interventions, VLSI implementation of neural networks, and interdisciplinary education.

• Applied Geometry
• Discrete Differential Geometry
• Numerical Analysis
• +۳ مورد دیگر

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.

• Fluid-Structure Interaction
• Isogeometric Analysis
• Nonlinear Structural Mechanics
• +۲ مورد دیگر

Thomas Spenke is a Researcher at the Chair for Computational Analysis of Technical Systems (CATS) within RWTH Aachen University's Center for Computational Engineering Science. He joined the research team in October 2017 after completing his master's degree in Computational Engineering Science at RWTH Aachen, where his academic journey began in 2011 with early engagement in CATS through a 2014 project thesis on isogeometric shell elements. His educational background includes: Studies in Computational Engineering Science at RWTH Aachen University (2011–2017) Bachelor and seminar theses on isogeometric shell elements for fluid-structure interaction Master thesis on numerical methods enhancing partitioned fluid-structure interaction algorithms Dr. Spenke's research spans Fluid-Structure Interaction , Isogeometric Analysis , Nonlinear Structural Mechanics (specializing in Shell Theory), and Interface Quasi-Newton Methods . His work develops efficient computational frameworks for multi-physics problems, emphasizing partitioned approaches that decouple fluid and structural solvers while ensuring stability and convergence through advanced acceleration techniques. His 9 publications (2020–2024) reveal a consistent focus on optimizing partitioned fluid-structure interaction simulations. Key trends include quasi-Newton acceleration for interface coupling, adaptive time-stepping strategies, and spline-based space-time finite elements for moving domains. His research frequently addresses challenges in fully enclosed systems and lubricated contact scenarios, demonstrating strong interdisciplinary links between computational mathematics and mechanical engineering. As an academic advisor, Dr. Spenke has supervised four student projects: J. Zwar (2019): Adaptive time stepping for fluid-structure interaction J. Spatka (2020): Fluid-structure-contact interaction in lubricated temper rolling M. Velioglu (2021): Space-time spline-based finite elements for moving domains S. Woyda (2023): Adaptive time stepping for multi-field problems The Chair for Computational Analysis of Technical Systems operates within RWTH Aachen's Center for Computational Engineering Science, founded by Professor Marek Behr in 2004. Located in the Rogowski building since 2009, CATS focuses on computational methods for engineering systems with expertise in fluid dynamics, structural mechanics, and multi-physics coupling algorithms.

• Wavelets and Frames
• Harmonic and Functional Analysis
• Fractal Geometry
• +۳ مورد دیگر

Peter Massopust is a Privatdozent at the Technical University of Munich (TUM), where he is affiliated with the School of Computation, Information and Technology and the Department of Mathematics. His research spans multiple areas of mathematical analysis with a focus on fractal geometry, wavelet theory, and approximation methods. His educational background includes: Habilitation in 2011 from Technical University of Munich Ph.D. in Applied Mathematics from Georgia Institute of Technology (1986) MS in Mathematics from Georgia Institute of Technology (1985) MS in Physics from Georgia Institute of Technology (1981) Dr. Massopust's research interests primarily focus on Wavelets and Frames, Harmonic and Functional Analysis, Fractal Geometry and Fractal Interpolation Theory, and Splines and Approximation Theory. His work bridges theoretical mathematics with practical applications in signal processing, image analysis, and computational methods. His approach often combines classical mathematical techniques with innovative fractal-based methods to solve complex problems in approximation theory and functional analysis. His research has significantly contributed to the development of fractal interpolation functions, complex splines, and wavelet theory, with applications spanning from pure mathematics to engineering problems. His publication record demonstrates a consistent focus on fractal-based mathematical methods, with recent work expanding into quaternionic analysis, complex B-splines, and applications in signal processing. His research shows a clear trajectory from foundational work in fractal geometry to increasingly sophisticated applications in multidimensional signal analysis and computational mathematics. His scientific achievements have been recognized through several prestigious awards: Fulbright Scholarship (1980-1981) GIAN (Global Initiative for Academic Network) Award from the Republic of India (2016, 2017) Dr. Massopust has secured substantial research funding from various national and international sources, including the German Research Foundation (DFG), Bayerische Forschungsallianz, VolkswagenStiftung, and collaborations with Sandia National Laboratories and the National Science Foundation. His research program has consistently focused on advancing mathematical methods for signal and image processing, with particular emphasis on fractal-based approaches and wavelet theory. He has also been instrumental in fostering international collaborations, particularly through the EuroTech network and with institutions in Australia and India. Among his notable contributions is the GHM (Geronimo-Hardin-Massopust) Scaling Vector and DGHM (Donovan-Geronimo-Hardin-Massopust) Multiwavelet, developed at the Georgia Tech Research Institute in 1995. This work has had significant impact in the field of wavelet analysis and its applications.

Simon Adrianمشاهده پروفایل

استاد

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +۳ مورد دیگر

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

Jürgen Bechtloffمشاهده پروفایل

استاد

• Mechatronics
• Automation Technology
• Digital Production
• +۳ مورد دیگر

Prof. Dr.-Ing. Jürgen Bechtloff serves as Professor of Measurement, Control and Regulation Technology at the South Westphalia University of Applied Sciences since 1998. He has held significant administrative roles including Founding Dean of Mechanical and Industrial Economics (2002-2004), Dean of Engineering and Economics (2004-2012), and Head of Scientific Center for Dual Studies and Continuing Education (2012-2019). His academic career spans industrial experience at Klöckner-Moeller GmbH (1992-1998) and academic research at TU Braunschweig (1987-1992). Education: Diplom from TU Braunschweig (1987), Dr.-Ing. from TU Braunschweig (1992) Laboratory: Operates the TransferFactory Industry 4.0 demonstrator since 2013 Research Focus: Industrial automation systems, particularly in Industry 4.0 implementation, Digital Production methodologies, and Mechatronic Systems simulation. His work emphasizes practical solutions for real-world challenges in: IoT gateway and platform integration Feasibility studies and implementation support Control engineering and measurement technology Electronics cam systems for motion control Robotic programming and interpolation methods Publication Trends: 15 most recent publications (2000-2020) demonstrate consistent focus on Industry 4.0 implementation, Mechatronics , and Digital Production technologies. His work bridges theoretical concepts with practical applications through case studies and demonstrator projects like the TransferFactory system. Teaching Contributions: Coordinates courses in Mechatronic Systems and Simulation and Digital Production for both bachelor and master programs, with emphasis on project-oriented learning and practical implementation of automation concepts using modern tools like MATLAB/Simulink. Technology Transfer: Provides services in measurement technology (data processing/visualization), control engineering (simulation and implementation), and mechatronics (3D motion design, kinematic analysis). Operates with state-of-the-art equipment including Beckhoff TwinCAT, Siemens S7/TIA systems, ABB/Kuka robots, and MES platforms.

• Mathematical Methods in Signal Processing
• Data Science
• Dynamical Systems
• +۵ مورد دیگر

Nada Sissouno is a Professor of Mathematics and Didactics of Mathematics at the Faculty of Electrical Engineering, Media and Informatics at Amberg-Weiden University of Applied Sciences since November 2023. She also serves as Vice Dean and Co-head of the Competence Center Grundlagen (CCG). Additionally, she maintains a position as a guest researcher at the Research Group: Applied and Numerical Analysis and Optimization and Data Analysis at the Technical University of Munich (TUM). Her educational background includes a Doctorate in Mathematics (Dr. rer. nat.) from TU Darmstadt (2007-2011) and a Diplom in Mathematics with a minor in psychology from TU Darmstadt (2000-2007). She has completed further education as a Diversity Manager in 2021 and holds certificates in teaching in higher education from the Bavarian Universities (2014-2016). Professor Sissouno's research focuses on mathematical methods in signal and image processing, data science, dynamical systems, numerical simulation, and approximation theory. Her work particularly emphasizes spline functions on domains, wavelets and frames, and evidence-based development of teaching methodologies. Her recent publications demonstrate strong expertise in mathematical imaging, phase retrieval problems, and approximation theory, with applications spanning ptychographic imaging, variational inpainting methods, and structural sparsity in multiple measurements. Her collaborative research bridges theoretical mathematics with practical applications in signal processing and image analysis, with a particular focus on developing robust numerical algorithms for complex data analysis problems. She has published in prestigious journals including Advances in Computational Mathematics, Journal of Fourier Analysis and Applications, IEEE Transactions on Signal Processing, and Inverse Problems. Referentin für Talentmanagement & Diversity at TUM (2022-2023) Deputy spokesperson of Research Associates' Council of the TUM (2019-2023) Gender equality officer of Department of Mathematics (2019-2022) Professor Sissouno teaches mathematics courses for engineering and computer science students, with a focus on making mathematical concepts accessible and relevant to practical applications. She has been involved in teacher training and the evidence-based development of teaching methodologies, demonstrating her commitment to both research excellence and educational innovation.

• Statistics
• Applied Mathematics
• Systems Biology
• +۲ مورد دیگر

Dr. Hannes Matuschek is a Researcher in the Department of Applied Mathematics at the University of Potsdam, Germany, with office space 2.09.1.24 and contact email hannes.matuschek@uni-potsdam.de. He actively participates in the institute's academic events including working group seminars and colloquia. Research Interests: His work spans Statistics, Applied Mathematics, Systems Biology, and Biomechanics. Key contributions include statistical methodology for linear mixed models (addressing Type I error/power tradeoffs and interaction effects), smoothing spline ANOVA for eye movement analysis in reading, stochastic modeling of gene regulatory networks, and vector field manipulations on spherical domains. His research bridges theoretical frameworks with applications in cognitive science, systems biology, and fraud detection. Publication Trends: His 12 publications (2012-2019) reveal a trajectory from computational systems biology (stochastic biochemical kinetics tools like iNA) toward statistical methodology development. Early work focused on noise approximation in gene networks, evolving into eye-tracking analysis and vector field mathematics, demonstrating consistent integration of advanced statistics with domain-specific challenges across biological and cognitive sciences. Scientific Awards: No awards were mentioned in the provided materials. Advising and Grants: No information was provided regarding student supervision, grant funding, or research collaborations. Labs and Teams: He operates within the Applied Mathematics research group at the University of Potsdam, contributing to seminars and events in analysis and interdisciplinary applications as evidenced by his institutional presence.

• Statistics
• Functional Data Analysis
• Aggregation Theory

Prof. Dr. Alois Kneip is a leading academic at the Department of Economics , University of Bonn, with affiliations to the Institute for Finance & Statistics and the Hausdorff Center for Mathematics . His research focuses on advanced statistical methodologies, particularly in Functional Data Analysis , Aggregation Theory , and Nonparametric Statistics . His work spans interdisciplinary applications, including econometric modeling, growth curve analysis, and high-dimensional regression. Key contributions include developing frameworks for Malmquist indices , DEA efficiency scores , and functional principal component analysis . Recent publications emphasize spatial regression, parameter cascading, and reconstruction of fragmented functional data. Alois Kneip’s research has been published in top-tier journals such as the Journal of the American Statistical Association , Annals of Statistics , and Econometric Theory . His methodological innovations bridge theoretical statistics with practical problems in economics, finance, and biomedical data analysis. He actively contributes to teaching and academic leadership at the University of Bonn.

Ullika Scholzمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Fluid Dynamics
• Computational Engineering
• +۵ مورد دیگر

Ullika Scholz is a Ph.D. candidate at the Institute of Applied Mathematics (ACoM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. Ph.D. candidate (03/2020–present) M.Sc. in Mathematics (Free University of Berlin, 2019) B.Sc. in Mathematics (Free University of Berlin, 2014) Her research focuses on dimensionally reduced equation systems for incompressible free surface flows, extending moment approximations and model cascades for shallow flow. She works with PDE systems derived via vertical polynomial expansions, incorporating dispersive effects beyond hydrostatic pressure assumptions. Her scientific work intersects applied mathematics, computational fluid dynamics, and numerical analysis. She has participated in international conferences such as ICCE 2024 and MMKT IV, presenting research on shallow moment equations and spline basis functions for kinetic theory models. Rosa-Luxemburg-Foundation Scholarship recipient (2013–2019)

Gerlind Plonkaمشاهده پروفایل

استاد

• Numerical Fourier Analysis
• Wavelet Theory
• Regularization Methods
• +۳ مورد دیگر

Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation