جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Robotics
• Knowledge Representation
• +۳ مورد دیگر

Axel Großmann is a former PhD Researcher affiliated with the International Center for Computational Logic (ICCL) at the Technische Universität Dresden . He was part of the Faculty of Computer Science and the Knowledge Representation and Reasoning Research Group . His work focuses on robotics , particularly in areas such as mobile robot localization, control systems, and probabilistic sensor models. He contributed to integrating reactive control, reasoning, and execution monitoring in dynamic environments. Key research interests include knowledge representation , symbolic dynamic programming , and robot-environment interaction . His publications span topics like semi-supervised learning methods, visual sensor models, and robust localization techniques using Hough transforms and probability grids. He collaborated on technical reports and conference papers addressing autonomous navigation and adaptive control systems. While no awards or current students are explicitly mentioned, his involvement in the ICCL and the Knowledge Representation group highlights his contributions to advancing computational logic and robotics applications. His work integrates theoretical foundations (e.g., Fluent Calculus) with practical robotics challenges.

Andreas Alvermannمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +۷ مورد دیگر

Dr. Andreas Alvermann is a researcher at the Institute of Physics , University of Greifswald, Germany. His work spans quantum physics, condensed matter theory, and computational methods, with a focus on non-Hermitian systems, Floquet dynamics, and polaronic effects. Develops advanced numerical techniques for eigenvalue problems and quantum transport Investigates symmetry-protected topological phases in photonic systems Applies Chebyshev expansions to quantum impurity problems and disordered systems His research areas include: Non-Hermitian quantum mechanics Topological materials and edge states Quantum-classical crossover phenomena Optomechanical stability and chaos Electron-phonon coupling in polarons Stochastic Green's function methods Recent publication trends emphasize non-Hermitian topological phases (2021-2020), Floquet system engineering (2019-2020), and quantum transport in nanostructures (2015-2010). His collaborations with H. Fehske and G. Wellein highlight interdisciplinary computational physics efforts.

Axel Krönerمشاهده پروفایل

استاد

• Optimal Control
• Optimization with partial differential equations
• Numerical analysis
• +۳ مورد دیگر

Prof. Dr. Axel Kröner is a Professor of Optimization at the Institute of Mathematics, Martin-Luther-Universität Halle-Wittenberg, within the Faculty of Natural Sciences II. His research focuses on optimal control and numerical methods for partial differential equations (PDEs), stochastic control, and feedback mechanisms in complex systems. Academic Background: Ph.D. in Mathematics, Technische Universität München (2011) Diploma in Mathematics, Ruprecht-Karls-Universität Heidelberg (2007) Axel Kröner's research spans PDE-constrained optimization, numerical analysis, and control theory. Key areas include feedback control for hyperbolic and parabolic equations, stochastic optimal control for infinite horizon problems, and adaptive finite element methods for PDE discretization. His work bridges theoretical and computational aspects, with applications in elasticity, fluid dynamics, and quantum mechanics. Recent publications highlight advancements in Hamilton-Jacobi-Bellman equations for feedback control, bilevel optimization for parameter learning, and Runge-Kutta-based neural networks . Methodologies often integrate dynamic programming , Galerkin approximations , and error estimation for efficient solutions. Advising and Projects: He has supervised numerous Master's and Bachelor's theses on topics like wave equation control, inverse problems, and neural network optimization. Funding includes support from the German Academic Exchange Service (DAAD) and the Gaspard Monge Program for Optimization and Operations Research (2015-2018). Professional Activities: Kröner has organized major conferences such as the International Workshop on PDE Constrained Optimization and contributed to teaching at institutions including Humboldt-Universität zu Berlin and University Paris-Saclay.

Jakob Zechمشاهده پروفایل

استاد

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +۴ مورد دیگر

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

• Quantitative Finance
• Computational Chemistry
• Computational Biology
• +۳ مورد دیگر

Dr. Chiheb Ben Hammouda is an Assistant Professor at the Mathematical Institute of Utrecht University, specializing in mathematical modeling, numerical analysis, and computational simulation methods. His research intersects stochastic processes, uncertainty quantification, and machine learning, with applications in quantitative finance, computational biology, and renewable energy systems. Education: Postdoctoral researcher at RWTH Aachen University, PhD in Applied Mathematics and Computational Science, and Diplomingenieur (Master's equivalent). His work focuses on developing advanced numerical techniques such as hierarchical adaptive sparse grids, quasi-Monte Carlo methods, and stochastic optimal control for problems in finance and biology. Recent publications highlight applications in option pricing, extreme climate event modeling, and energy systems optimization. Dr. Hammouda has co-taught courses at RWTH Aachen University on stochastic numerics, machine learning integration in simulations, and partial differential equations. His expertise bridges theoretical mathematics and practical computational solutions across interdisciplinary domains.

• Wireless Communication Security
• Internet of Things
• Machine Learning Applications
• +۵ مورد دیگر

Wen Chen is a Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, with significant research contributions across multiple disciplines including wireless communication security, Internet of Things, and machine learning applications. Their work demonstrates strong collaborations with institutions including Chinese Academy of Sciences, Wayne State University, and various medical research centers. Research interests focus on cutting-edge applications of machine learning to solve real-world problems in cybersecurity, medical imaging, and energy systems. Recent work bridges theoretical computer science with practical implementations, particularly in federated learning approaches for resource-constrained environments and innovative security mechanisms for IoT devices. The research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and medical applications. Publication trends show increasing focus on practical implementations of theoretical concepts, with particular emphasis on security applications for wireless networks and medical diagnostics. Recent work integrates multiple machine learning paradigms to address challenges in non-IID data environments, sparse labeling scenarios, and resource-constrained hardware implementations. Current research directions include advancing secure communication protocols for IoT devices, developing efficient medical diagnostic tools using AI, and optimizing energy systems through intelligent prediction and control mechanisms. The work consistently demonstrates strong technical depth while maintaining practical applicability to real-world challenges.

Dirk Pflügerمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Peter Oswaldمشاهده پروفایل

استاد

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +۷ مورد دیگر

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

David Bommesمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Mesh Generation
• +۴ مورد دیگر

Prof. Dr. David Bommes is a leading researcher in computer graphics and geometry processing, currently a Professor at the University of Bern . His expertise lies in mesh generation, particularly quadrilateral and hexahedral meshing, numerical optimization, and automatic differentiation techniques. His research focuses on developing robust algorithms for generating high-quality meshes from complex geometries, with applications in CAD, architecture, and simulation. He has made significant contributions to the fields of surface and volume parametrization, directional field synthesis, and geometry processing optimization. Prof. Bommes has received notable recognition, including the Best Paper Award (1st place) at SGP 2022 and the Graphics Replicability Stamp for his work on TinyAD, a lightweight automatic differentiation library for geometry processing. His publications span top-tier venues such as SIGGRAPH, Eurographics, and ACM Transactions on Graphics, covering topics from automatic differentiation and geodesic computation to advanced meshing techniques. He actively collaborates with leading institutions and researchers worldwide.

Ivana Jovanovic Buhaمشاهده پروفایل

پژوهشگر

• Uncertainty Quantification
• Hydrology
• Bayesian Inference
• +۶ مورد دیگر

Ivana Jovanovic Buha is a Researcher and Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing in Computer Science (SCCS) since June 2018. She holds an M.Sc. (Honours) in Computational Science and Engineering from TUM (2018) and a Diploma in Electrical Engineering from the University of Belgrade (2014). Her research focuses on applied computational mathematics, particularly uncertainty quantification (UQ), inverse problems, and data-driven modeling, with applications in hydrology. Research Interests: Her work bridges theoretical UQ methods with real-world hydrological challenges. Key areas include high-dimensional UQ, sensitivity analysis of distributed hydrologic models, sparse grids methods, Bayesian inversion, and machine learning integration. She emphasizes practical applications such as rainfall-runoff modeling and parameter calibration of complex hydrological systems. Teaching: She has taught courses like Algorithms for Uncertainty Quantification and Data Mining , and supervised numerous student projects on topics such as Bayesian filtering, MCMC methods, and deep learning in hydrology. Awards: She received the Best Poster Award (1st place) at CoSaS 2018 for her work on 3D deep learning in point cloud analysis. She also organizes the BGCE Student Paper Prize at SIAM CSE conferences. Student Projects: Current and past advisees include studies on combining Bayesian filtering with polynomial chaos expansions, efficient Bayesian inference for hydrological parameters, and sparse grids applications. She actively mentors students in developing tools like pyApprox and UM-Bridge for UQ simulations. Labs/Teams: She collaborates within SCCS under Prof. Bungartz, contributing to projects on computational methods for environmental science and high-performance computing.

• Mobile Crowd Sensing
• Visual Information Processing
• Medical Imaging Analysis
• +۵ مورد دیگر

Huihui Chen is an Associate Professor in the Department of Computer Science and Engineering at Northwestern Polytechnical University, specializing in mobile crowd sensing, medical imaging analysis, and control systems for autonomous vehicles. With over 50 publications spanning from 2013 to 2025, Dr. Chen has established a significant research presence in interdisciplinary areas combining computer science, engineering, and medical applications. Dr. Chen's research interests focus on mobile crowd sensing systems , particularly visual crowdsensing applications for smart cities and transportation. Their work extends to medical imaging analysis for conditions like Fragile X syndrome and granulosa cell tumors, as well as control systems for unmanned surface vehicles. The research demonstrates a progression from fundamental crowd sensing frameworks to sophisticated applications in medical diagnostics and autonomous systems. Key methodological approaches include sensor fusion techniques, neural network modeling, and optimization algorithms for dynamic environments. Recent publications reveal a growing emphasis on interdisciplinary applications, particularly at the intersection of computer vision and medical diagnostics. The 15 most recent articles demonstrate expertise across multiple domains including transportation systems optimization, neural network synchronization, and medical imaging analysis. This diverse portfolio shows a research trajectory that bridges theoretical computer science with practical applications in healthcare and autonomous systems. Dr. Chen actively collaborates with researchers including Bin Guo (31 joint publications), Zhiwen Yu, Chundi Zheng, and Aiguo Wang, forming a productive research group focused on mobile sensing technologies. The collaborative network extends across multiple Chinese institutions, with significant contributions to IEEE journals and international conferences in computer science and engineering.

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۴ مورد دیگر

Wei Cui is a Professor in the School of Automation Science and Engineering at South China University of Technology, with significant research contributions across artificial intelligence, signal processing, and remote sensing applications. Their work demonstrates strong interdisciplinary collaboration across engineering disciplines. Research interests span machine learning for multimodal data analysis, with particular expertise in fake news detection systems, transportation optimization, and remote sensing applications. Recent work shows increasing focus on robotics perception, quantum information processing, and healthcare technology applications. The research program integrates theoretical advances with practical implementations in real-world systems. Publication trends over the last three years reveal growing specialization in multimodal learning approaches, with approximately 40% of recent publications focusing on AI applications for social media analysis and disinformation detection, 25% on transportation and logistics optimization, 20% on remote sensing and environmental monitoring, and 15% on emerging applications in healthcare and robotics. Wei Cui's research has been supported through multiple collaborative projects addressing practical engineering challenges in urban infrastructure, healthcare logistics, and environmental monitoring systems. The work demonstrates strong industry-academia collaboration with practical implementations in power systems, transportation networks, and healthcare technologies. Current research directions include advancing multimodal perception systems for humanoid robots, developing more robust disinformation detection frameworks, and exploring quantum-inspired optimization techniques for complex engineering problems. The lab maintains active collaborations with researchers in China, the United States, and Europe across multiple engineering disciplines.

Christoph Pflaumمشاهده پروفایل

استاد

• Numerical Analysis
• Optics
• Laser Engineering
• +۵ مورد دیگر

Prof. Dr. Christoph Pflaum holds the Professorship for Numerical Simulation and High-Performance Computing at the Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg. His research spans computational optics, solid-state laser simulation, and sustainable energy systems. Key Research Areas: Numerical methods for PDEs, thermal lensing in lasers, polarization effects, and solar energy harvesting technologies. Recent Work: Focus on adaptive sparse grids for high-dimensional PDEs, 3D laser pulse amplification models, and optimization of solar-integrated airships. Scientific Trends: His publications emphasize computational modeling of optical phenomena (thermal lensing, Kerr effects) and renewable energy innovations (thin-film solar cells, flexible solar arrays). Collaborative projects include SiSoFlex and LightWave, integrating simulations with industrial applications. Collaborations: Works with institutions in Germany and internationally, including teams in Erlangen, Strasbourg, and Lisbon. Current projects align with sustainable aviation and laser engineering.

Malte von Schevenمشاهده پروفایل

پژوهشگر

• Adaptive structures
• Fluid-structure interaction
• Software Engineering
• +۳ مورد دیگر

Malte von Scheven is a Senior Researcher and Deputy Director at the Institute of Structural Analysis and Dynamics at the University of Stuttgart. He holds a Dr.-Ing. degree (2009) and specializes in adaptive structures, fluid-structure interaction, and computational mechanics. Research Focus: Redundancy matrices for structural assessment, high-performance computing, actuator placement optimization Teaching: Finite element methods, computational mechanics, nonlinear structural analysis Leadership: Deputy Director since 2006, conference organizer for ECCOMAS and SMART symposia His work bridges structural mechanics with bio-inspired design, including studies on sea urchin skeletons as models for segmented shells. He has supervised numerous theses on SFRP composites, topology optimization, and adaptive systems. Scientific Engagement: Published 15+ papers on redundancy matrices and FSI Organized mini-symposia at international conferences (ECCOMAS 2024, SMART 2023) Active in university governance through Faculty Council and TIK committee Recent research investigates mechanical modeling of adaptive structures, with applications in civil engineering and architectural geometry. His redundancy matrix framework provides novel performance indicators for robust design and assemblability assessment.