جست‌وجوی استاد و پژوهشگر

Artem Sapozhnikovمشاهده پروفایل

استاد

• Probability theory
• percolation
• random walks
• +۱ مورد دیگر

Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.

• Optimal Control of Partial Differential Equations
• Monte Carlo Methods
• Numerical Analysis of Hyperbolic Differential Equations
• +۳ مورد دیگر

Dr. Jan Bartsch is a Lecturer at the Institute of Mathematics , University of Würzburg, specializing in optimal control theory, kinetic models, and numerical methods. His work spans stochastic differential equations, Monte Carlo frameworks, and applications to physics and computational mathematics. Education : PhD in Mathematics (2021-2024, University of Würzburg), Master’s in Mathematics (2016-2018), Bachelor’s in Computational Mathematics (2013-2016). Research interests : Focus on optimal control of partial differential equations, stabilization of kinetic models, and computational methods for stochastic systems. Publications : Recent works include adjoint-based control of jump-diffusion processes, nonlinear operator reconstruction, and Monte Carlo frameworks for plasma control. Collaborations : Works with Stefan Volkwein (University of Konstanz) and Alfio Borzi (University of Würzburg). Skills : Proficient in C/C++, Python, MATLAB, and ParaView for computational tasks.

• Optimal Control Theory
• Numerical Methods
• Kinetic Models
• +۵ مورد دیگر

Dr. Jan Bartsch is a Lecturer at the Institute of Mathematics, University of Würzburg, Germany. He has been working as a Scientific Employee at the Institute of Mathematics since 2024. Prior to this, he was a Postdoc at the University of Konstanz from 2021 to 2024 in the Collaborative Research Center 1432 "Fluctuations and Nonlinearities in Classical and Quantum Matter Beyond Equilibrium." Dr. Bartsch's educational background includes: PhD in Scientific Computing from the University of Würzburg (2018-2021), dissertation title: "Theoretical and numerical investigation of optimal control problems governed by kinetic models" Master's degree in Mathematics from the University of Würzburg (2016-2018), thesis title: "Optimal control problems governed by Liouville models - Mathematical analysis and implementation" Bachelor's degree in Computational Mathematics from the University of Würzburg (2013-2016), thesis title: "Optimal Control of Androgen Suppression of Prostate Cancer" His research focuses on optimal control theory, numerical methods for partial differential equations, and kinetic models. Dr. Bartsch specializes in Monte Carlo methods for solving kinetic models, optimal control of stochastic differential equations, and numerical methods for hyperbolic differential equations. His work bridges theoretical mathematics with practical computational approaches, particularly in the context of control problems governed by complex physical models. Dr. Bartsch's recent publications show a strong focus on applying adjoint-based methods to optimal control problems, particularly involving stochastic processes and kinetic models. His research demonstrates expertise in developing numerical frameworks that combine Monte Carlo approaches with control theory to solve complex mathematical problems arising in physics and engineering applications. Contact Information: Email: jan.bartsch@uni-wuerzburg.de Phone: +49 931 31-80733 Office: Building 40 (Mathematics East), Room 00.012, Emil-Fischer-Straße 40, 97074 Würzburg

Maria Antonia Brovelliمشاهده پروفایل

استاد

• Geomatics
• GIS
• Open Source GIS
• +۶ مورد دیگر

Maria Antonia Brovelli is a Professor of GIS at Politecnico di Milano (PoliMI) in the Department of Civil and Environmental Engineering and a member of the School of Doctoral Studies in Data Science at Roma La Sapienza University. Formerly Vice-Rector of PoliMI for the Como Campus (2011-2016), she currently leads the GEOLab (Geomatics and Earth Observation Lab) and holds influential roles including Deputy Chair of the ISPRS TC on Spatial Information Science, co-chair of the United Nations Open GIS Initiative, and Chair of the UN-GGIM Academic Network. Her research spans geomatics with evolving expertise from geodesy and radar-altimetry to GIS, webGIS, geospatial web platforms, Volunteered Geographic Information (VGI), Citizen Science, Big Geo Data, and geospatial AI. A global leader in Open-Source GIS advocacy, her work emphasizes open data ecosystems, collaborative mapping, and AI-driven geospatial solutions for environmental challenges. Key methodologies include open-source software frameworks and citizen science integration. Recent publications (2025) demonstrate convergence of AI and geospatial analysis, featuring satellite-ground sensor fusion for urban air quality, high-resolution land cover mapping, zero-shot learning for remote sensing imagery, landslide prediction models, and climate-agriculture impact studies. Dominant themes include open geospatial platforms, transferable AI architectures, and multi-scale environmental monitoring using open data principles. Honors include: Sol Katz Award for Geospatial Free and Open Source Software (OSGeo) As mentor of PoliMI's YouthMappers chapter (PoliMappers), she guides student-led open mapping initiatives. Her editorial leadership as Associate Editor of ISPRS International Journal of Geo-Information shapes discourse in geospatial AI and open science. Research grants span ESA Earth Observation programs, UN initiatives, and international collaborative projects focused on open geospatial infrastructure. Brovelli directs GEOLab at Politecnico di Milano, a hub for geospatial innovation developing open-source tools for earth observation, citizen science platforms, and AI-driven spatial analytics. The lab collaborates with UN-GGIM, ISPRS networks, and ESA projects, driving open standards adoption globally while advancing geospatial AI applications in climate resilience and urban sustainability.

• Partial Differential Equations
• Optimal Control
• Quantum Control
• +۷ مورد دیگر

Prof. Dr. Alfio Borzi holds the Chair of Scientific Computing at the University of Würzburg's Institute of Mathematics. He specializes in computational mathematics, optimal control, and numerical analysis. His research focuses on partial differential equations (PDEs), quantum control, multigrid methods, and stochastic models. He has contributed extensively to the development of numerical methods for solving complex systems such as the Vlasov-Poisson and Kohn-Sham equations. Borzi has led multiple interdisciplinary projects, including collaborations with medical and engineering institutions, and has supervised numerous PhD students. His work spans applications in medical imaging, plasma physics, and ecological modeling. Education: Borzi earned his PhD in Mathematics from SISSA/ISAS (Trieste, Italy) and completed his habilitation at the University of Graz. He has held academic positions at institutions in Italy and Austria before joining Würzburg in 2011. Research Interests: Borzi’s expertise includes optimal control of PDEs, quantum systems, and stochastic processes. He develops numerical schemes for Fokker-Planck equations, kinetic models, and superresolution microscopy. His work integrates computational methods with real-world applications like wine fermentation optimization and MRI reconstruction. Awards and Grants: Although no specific awards are listed, Borzi has secured significant funding, including a BMBF grant for AI-driven MRI advancements and a BayFrance grant for plasma cooling optimization. His research also involves international collaborations, such as with French institutions on plasma control and Italian teams on synchronization models. Teaching and Mentorship: Borzi teaches courses on numerical methods, optimal control, and mathematical modeling. His team includes postdocs and students working on projects like neural network optimization and quantum control algorithms. He actively publishes in top journals and organizes workshops on multigrid methods and computational optimization.

Jürgen Frankeمشاهده پروفایل

استاد

• Nonlinear Time Series Analysis
• Stochastic Processes
• Statistical Methodology
• +۵ مورد دیگر

Prof. Dr. Jürgen Franke is a renowned academic in statistics and applied mathematics. He held the position of Professor C4 (Chair) for Applied Mathematical Statistics at Technische Universität Kaiserslautern from 1988 to 2017. Since 2017, he has been a Consultant Researcher at the Fraunhofer Institute for Industrial Mathematics (ITWM) in Kaiserslautern. His research focuses on nonlinear time series analysis, stochastic processes, and their applications in finance, risk management, and biomedical data analysis. Education: - Diplom in Mathematics, Goethe-Universität Frankfurt a.M., 1974. - PhD (Dr. phil.nat.) in Mathematics, Goethe-Universität Frankfurt a.M., 1980. - Habilitation in Mathematics, Goethe-Universität Frankfurt a.M., 1985. Research Interests: Franke's research emphasizes the development of statistical methodologies for nonlinear time series, spatial data, and stochastic processes. Key areas include local smoothing techniques (kernel and wavelet-based estimates), functional data analysis, resampling methods (bootstrap), and nonparametric approaches in machine learning. His applied work addresses challenges in finance, risk management, and biomedical data analysis where traditional methods may be insufficient. Advising and Grants: While specific grants are not detailed, Franke has collaborated extensively with researchers in applied mathematics and statistics, contributing to numerous projects. His work often intersects with interdisciplinary applications, reflecting a commitment to bridging theoretical and practical domains. Labs/Teams: Affiliated with the Department of Mathematics at TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), where he focuses on applied statistical research and collaboration with industry.

Christoph Thäleمشاهده پروفایل

استاد

• Stochastic Geometry
• Geometric Probability
• Limit Theorems
• +۴ مورد دیگر

Professor Christoph Thäle is a faculty member in the Faculty of Mathematics at Ruhr-Universität Bochum . His research focuses on stochastic geometry, geometric probability, limit theorems for random structures, and applications of Malliavin calculus and Stein's method. He has supervised numerous PhD and master's students, including Tristan Schiller, Nils Heerten, and Kathrin Meier. His work bridges theoretical probability with geometric analysis, particularly in high-dimensional spaces. Research Interests: Stochastic Geometry, Convex Geometry, Limit Theorems, Random Structures in High Dimensions Team Members: Postdocs (Panagiotis Spanos, Ercan Sönmez), PhD students (Philipp Tuchel, Bahareh Yousefi), and collaborators like Anna Gusakova and Zakhar Kabluchko. Key contributions include studies on random polytopes, Poisson hyperplane tessellations, and geometric analysis in hyperbolic spaces. His recent publications (2023–2025) explore intersection probabilities, radial spanning trees, and large deviations in high-dimensional settings. Thäle's research has been supported by collaborations with institutions like the University of Duisburg-Essen and the University of Osnabrück.

Günter Lastمشاهده پروفایل

استاد

• Stochastic Processes
• Stochastic Geometry
• Poisson Processes
• +۶ مورد دیگر

Prof. Dr. Günter Last is a Professor at the Institute of Stochastics within the Faculty of Mathematics at the Karlsruhe Institute of Technology (KIT) . He has held this position since 2000 (C4/W3 Professorship). Last's research focuses on stochastic processes , stochastic geometry , and their applications in mathematics, physics, and finance. Education: Diploma in Mathematics, Humboldt University Berlin (1984) PhD in Mathematics, Humboldt University Berlin (1987) Dr. Sc. in Mathematics, Technical University Braunschweig (1995) Research Interests: Last leads research in Poisson processes, Boolean models, Gibbs processes, and spatial random systems. His work explores Palm calculus, hyperuniformity, normal approximation, and percolation phenomena. He co-authored the textbook Lectures on the Poisson Process with Mathew Penrose. Publications & Trends: His recent work includes Normal approximation of Kabanov-Skorohod integrals , Hyperuniform stable matchings , and Poisson hulls , reflecting a focus on stochastic analysis and geometric probability. His research bridges theoretical insights with applications in digital microstructures and wireless network modeling. Scientific Leadership: Co-Editor for Electronic Journal of Probability (2018-2023) Speaker of DFG Research Unit Geometry and Physics of Spatial Random Systems (2011-2018) Associate Editor for Applied Probability Journals (2005-2019) Advising: Last has supervised 15 doctoral theses and numerous diploma/master's theses, including topics on neural networks, random tessellations, and stochastic financial models.

Norbert Henzeمشاهده پروفایل

استاد

• Statistical Goodness-of-Fit Tests
• Geometric Extreme Value Theory
• Multivariate Statistics
• +۲ مورد دیگر

Prof. Dr. Norbert Henze is a distinguished statistician at the Institute of Stochastics , Karlsruhe Institute of Technology . His work spans theoretical and applied statistics, with a focus on goodness-of-fit testing , geometric extreme value theory , and multivariate statistical analysis . Research Interests: Statistical Goodness-of-Fit Tests Geometric Extreme Value Theory Multivariate Statistics Key Contributions: Developed novel tests for multivariate normality using Fourier methods and partial differential equations Analyzed limit laws for nearest neighbor distances in high-dimensional spaces Explored stochastic phenomena in Pólya's urn model and random point distributions Students & Collaborations: Supervised 4 PhD students and 22 diploma/master's theses Collaborated with international experts (B. Ebner, M.D. Jiménez-Gamero, S.G. Meintanis) Recent Publications: 2024: Spherical data testing in Statistical Papers 2023: Weibull distribution tests in Annals of Institute of Statistical Mathematics 2022: Complex symmetry tests in TEST Notable Methodological Innovations: Unified empirical process approaches, harmonic oscillator-based normality tests, and geometric extreme value characterizations.

• Game Theory
• Microeconomic Theory
• Strategic Experimentation
• +۳ مورد دیگر

Professor Sven Rady holds the Hausdorff Chair for Mathematical Economics at the University of Bonn, where his research examines decision dynamics and equilibrium processes under uncertainty through economic agent experimentation. His scholarly focus spans Game Theory, Microeconomic Theory, and Strategic Experimentation, investigating learning mechanisms in bandit models, information externalities, and equilibrium dynamics. This work critically analyzes how agents navigate uncertainty in environments ranging from team-based free-riding scenarios to two-sided market platforms, yielding insights for industrial organization and market design. Analysis of his 2009-2019 publications reveals a cohesive research trajectory centered on strategic experimentation in bandit frameworks. Key contributions address free-riding mitigation, undiscounted payoff structures, private information effects, and Poisson bandit applications, consistently employing continuous-time modeling to dissect learning dynamics and equilibrium formation under uncertainty. No scientific awards were documented in the source material. Details regarding student advising, research grants, or laboratory affiliations were not provided in the available text.

Martin Huesmannمشاهده پروفایل

استاد

• Stochastic analysis
• Optimal transport
• Random measures
• +۱۰ مورد دیگر

Prof. Dr. Martin Huesmann is a full Professor at the Institute for Mathematical Stochastics, Department of Mathematics and Computer Science, University of Münster, and a key investigator in the Excellence Cluster Mathematics Münster. He leads an active research group focusing on stochastic analysis, optimal transport, random measures, and robust finance. Research Interests: Stochastic analysis Optimal transport (classical, martingale, causal) Random measures and point processes Skorokhod embedding and its geometric variants Matching and allocation problems Robust and model-independent finance Gradient flows and asymptotic analysis of random structures His recent publications reveal a strong trend in probabilistic approaches to optimal transport, particularly in martingale and multi-marginal settings, with deep connections to financial mathematics and geometric analysis of stochastic processes. His work often involves collaboration with leading researchers such as Mathias Beiglböck, Alex Cox, and Felix Otto. Scientific Contributions: Investigator in EXC 2044 - C1: Evolution and asymptotics, developing gradient flow descriptions for diffusion-reaction systems. Research on asymptotics of random polytopes and random trees. Development of transport distances for stationary random measures. He actively advises PhD and master’s students, including Bastian Müller, Jonas Jalowy, and Hanna Stange. He teaches advanced courses such as Optimal Transport , Stochastic Analysis , and Probability Theory , and supervises numerous master’s and bachelor’s theses. His research group includes current members Bastian Müller, Hanna Stange, and Jonas Jalowy, and former members Francesco Mattesini and Martin Brückerhoff, reflecting sustained research activity and mentorship.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Stochastic Differential Equations
• +۳ مورد دیگر

Professor Han Cheng Lie is a faculty member at the Institut für Mathematik , Universität Potsdam , specialising in Uncertainty Quantification . His research focuses on Bayesian inference, inverse problems, and probabilistic numerical methods. Contact: han.lie@uni-potsdam.de Location: Campus Golm, Haus 9, Karl-Liebknecht-Str. 24-25, D-14476 Potsdam OT Golm, Germany His work addresses mathematical challenges in Bayesian inverse problems , including stability analysis of posteriors, low-rank approximations for Gaussian posteriors, and randomized algorithms for differential equations. He has contributed to Markov chain Monte Carlo on manifolds and transition path theory for diffusion processes. Recent publications highlight advances in dimension-independent sampling , probabilistic integrators , and model error mitigation in Bayesian frameworks. His methodological innovations span applications in computational statistics , molecular dynamics , and geophysical fluid dynamics . He organizes academic events like the Potsdam Data Assimilation Days and has presented at international workshops including Oberwolfach and SIAM conferences .

Benedikt Jahnelمشاهده پروفایل

استاد

• Statistical Mechanics
• Stochastic Geometry
• Spatial Epidemiology
• +۳ مورد دیگر

Prof. Dr. Benedikt Jahnel is a Professor at the Institute of Mathematical Stochastics, Carl Friedrich Gauss Faculty, Technical University of Braunschweig, and Head of the Leibniz Junior Research Group on Probabilistic Methods for Dynamic Communication Networks. His research focuses on the modeling and analysis of spatially embedded systems with interacting random components, with applications in physics, epidemiology, and telecommunications. Education: PhD in Organismic and Evolutionary Biology from Ruhr University Bochum (2011), Diploma from Technical University of Berlin. Research interests: Uses tools from statistical mechanics and stochastic geometry to study phase transitions, percolation theory, and spatial random processes. Current projects investigate continuum percolation, interacting particle systems, and probabilistic methods for communication networks. Recent publications demonstrate strong emphasis on spatial stochastic processes and percolation theory, with applications to network connectivity and epidemic modeling. Trends include mathematical analysis of phase transitions in random environments and dynamics of communication networks. Scientific awards include leadership of Leibniz Junior Research Group, EURANDOM Ambassador appointment, and election to the board of the Probability and Statistics Group of the German Mathematical Society. Leads a research team including 3 postdocs and 3 predoctoral researchers. Current projects funded by DFG, ERC, and Math+ Cluster of Excellence. Heads the DYCOMNET research group at Weierstrass Institute Berlin, focusing on dynamic spatial random systems.

• Parallel and Distributed Systems
• High-Performance Computing
• Parallel Programming
• +۷ مورد دیگر

Prof. Dr. Thomas Rauber is a Professor at the University of Bayreuth in the Faculty of Mathematics, Physics and Computer Science, where he leads the Chair of Applied Computer Science II – Parallel and Distributed Systems. His research spans several decades with continuous scholarly output, demonstrating significant contributions to parallel computing, high-performance systems, and energy-efficient computation. Rauber maintains strong collaborative relationships with researchers including Gudula Rünger and Matthias Korch, with whom he has co-authored numerous publications. His primary research interests include parallel and distributed systems, high-performance computing, task scheduling, energy efficiency in computing, scientific computing with focus on Runge-Kutta methods and ODE solvers, and performance modeling. Rauber's work has evolved from foundational parallel programming concepts to contemporary concerns about energy consumption in computing systems. His research addresses both theoretical aspects of parallel algorithms and practical implementation challenges on modern architectures. Rauber's publication record shows a clear trend toward energy-aware computing, with recent work focusing on the trade-offs between performance, energy consumption, and solution accuracy. His 2023-2025 publications demonstrate continued innovation in task scheduling, software-defined environments for cloud applications, and optimization of numerical methods for modern multicore processors. His textbook "Parallel Programming for Multicore and Cluster Systems" (now in its third edition) has become a standard reference in the field. While specific grant information isn't detailed in the provided text, Rauber's extensive publication record across multiple decades suggests sustained research funding. His work on projects like TGrid (Runtime environment for heterogeneous systems and grid systems) and investigations into task pools for dynamic load balancing indicates involvement in significant research initiatives. Rauber maintains an active research laboratory focused on parallel and distributed systems, with ongoing projects examining communicating multiprocessor tasks, runtime environments for heterogeneous systems, and self-adaptation techniques for time-step-based simulations on heterogeneous HPC systems. His research group continues to produce influential work at the intersection of theoretical computer science and practical high-performance computing applications.

• Stochastic Analysis
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Dr. Petru Cioica-Licht is an Assistant Professor (Akademischer Rat) at the Institut für Mathematik, Universität Kassel, specializing in Stochastic Analysis and Applied Mathematics. His research focuses on stochastic partial differential equations (SPDEs), regularity theory in functional spaces (Sobolev/Besov), and numerical methods for high-dimensional problems. He holds a PhD from Philipps-Universität Marburg (2010-2013) and has held positions at the University of Otago (New Zealand) and the University of Duisburg-Essen. His work bridges theoretical analysis and computational approaches, addressing challenges in stochastic integration, domain geometry effects, and adaptive numerical schemes. Professional History: Since 2021 – Academic Council Member (Akademischer Rat), Universität Kassel. 2019–2021 – Researcher in Stochastic Analysis at Universität Duisburg-Essen. 2017–2018 – Lecturer (Assistant Professor) at University of Otago. 2016–2017 – Postdoctoral Fellow funded by the Marsden Fund (New Zealand). 2014–2015 – Postdoc in DFG-funded project on SPDE regularity in quasi-Banach spaces. Research Interests: SPDEs on non-smooth domains, regularity theory in weighted spaces, numerical approximation of high-dimensional PDEs, and adaptive wavelet methods. His work emphasizes the interplay between stochastic processes and functional analytic techniques, particularly in analyzing solution regularity and developing efficient computational frameworks. Publications Trends: Recent articles address Sobolev/Besov regularity for SPDEs on angular domains, stochastic integration in quasi-Banach spaces, and overcoming the curse of dimensionality via deep neural networks. Earlier work includes Besov regularity for Navier-Stokes equations and convergence analysis of adaptive Rothe methods. Grants & Funding: Recipient of DFG and Marsden Fund support for projects on SPDE regularity and stochastic fluctuations. Active contributor to collaborative research in numerical analysis and stochastic processes. Labs/Teams: Part of the Analysis and Applied Mathematics group in Stochastics at Universität Kassel, focusing on interdisciplinary applications of stochastic and functional analytic methods.