جستجوی استاد راهنمای دانشگاهی

Olga Mula is an Associate Professor of Mathematics at TU Eindhoven and will transition to a Full Professor position at the University of Vienna starting September 2025. Her research focuses on numerical analysis of Partial Differential Equations (PDEs) combined with data-driven methodologies, including Scientific Machine Learning, High-Dimensional Approximation, and Optimal Transport. She leads the Computational PDEs group and has held roles at institutions like INRIA Paris and Paris Dauphine University. Mula's work addresses challenges in computational science, such as pollution modeling, epidemiology, and nuclear engineering. She currently seeks PhD and Postdoc candidates for projects on Wasserstein Gradient Flows and singularly perturbed PDEs. Education: PhD in Applied Mathematics (Sorbonne University, 2014), French Habilitation (Paris Dauphine University, 2021) Research Interests: Machine Learning for PDEs, Data Assimilation, Numerical Optimal Transport Her contributions include advancements in PINN frameworks, model order reduction, and sensor placement strategies. She teaches courses on Numerical Analysis, Optimization, and Mathematics of Neural Networks at TU Eindhoven.

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +۳ مورد دیگر

Marcel Nutz is a Professor at Columbia University in the Department of Statistics , with secondary affiliations in the Department of Mathematics and the Data Science Institute . He holds a PhD in Mathematics from ETH Zurich and is recognized for his contributions to quantitative finance and optimal transport . His research integrates stochastic control , game theory , and machine learning to address problems in financial markets and probabilistic modeling. Key themes include martingale transport , regularized optimal transport , and mean-field analysis , with applications to market microstructure and risk management . Marcel’s recent work focuses on regularization techniques to mitigate the curse of dimensionality in optimal transport, stability analysis of variational methods, and martingale Schrödinger bridges for portfolio optimization. His publications emphasize numerical algorithms like Sinkhorn’s method and theoretical guarantees for financial models. Scientific Awards: IMS Medallion Award Alfred P. Sloan Fellowship IMS Fellow Marcel acknowledges support from five NSF grants and the Center for Digital Finance and Technologies . He currently serves on the editorial boards of Annals of Applied Probability (AAP), Mathematical Finance (MF), and others, with past roles at Stochastic Processes and their Applications (SPA) and Finance and Mathematics (FMF).

• Quantitative Finance
• Optimal Transport
• Stochastic Control
• +۶ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Jan Glaubitz is an Assistant Professor in Scientific Computing at the Department of Mathematics, Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) within the Faculty of Science and Engineering. His research lies at the intersection of numerical analysis, inverse problems, uncertainty quantification, and computational fluid dynamics, with a focus on developing robust and provably convergent computational methods. His research interests center on advancing Bayesian Scientific Computing , particularly for hyperbolic conservation laws and inverse problems under uncertainty. He works on establishing rigorous theoretical foundations—such as approximation, convergence, and stability—for numerical methods while quantifying confidence in computational predictions. His work integrates high-order numerical schemes, shock-capturing techniques, data assimilation, and uncertainty quantification to solve complex problems in science and engineering. The recent publications highlight a strong trend in developing mathematically rigorous and computationally efficient methods for partial differential equations and Bayesian inference. His work bridges theoretical numerical analysis with practical computational frameworks, focusing on stability-preserving discretizations and sparse reconstructions in inverse problems. Jan Glaubitz has held postdoctoral positions at the Massachusetts Institute of Technology (MIT) under Youssef Marzouk and at Dartmouth College under Anne Gelb, reflecting a strong international research trajectory. Although no specific grants or students are listed, his active publication record and leadership in method development suggest ongoing research funding and academic mentorship. He is associated with the Division of Applied Mathematics at Linköping University, which conducts research in computational mathematics, mathematical statistics, and optimization, providing a collaborative environment for interdisciplinary scientific computing.

• Scientific Computing
• Bayesian Scientific Computing
• Numerical Analysis
• +۷ مورد دیگر

Claes Eskilsson is a researcher at Chalmers University of Technology , specifically in the Department of Mechanics and Maritime Sciences under the Marine Technology division. His work focuses on computational fluid dynamics (CFD) , wave energy converters , and mooring system dynamics for marine applications. Research Projects: MIDWEST: Multi-fidelity decision tools for wave energy systems (2015-2018) Assessment of tidal turbine noise pollution (2015-2016) Including nonlinear/viscous effects in wave energy modeling (2015-2017) Forankringslösninger for wave energy devices (2015-2017) SDWED: Structural design of wave energy devices (2013-2014) His research interests include: Computational modeling of marine systems Wave energy converter hydrodynamics High-order numerical methods (spectral/hp elements, Discontinuous Galerkin) Cavitation and erosion analysis in marine flows Multiphysics modeling of floating structures The publications span topics in wave energy converter dynamics, mooring system analysis, and CFD methodology. Key trends include 2013-2015 developments in spectral/hp element methods for coastal engineering, and 2015-2020 advancements in multi-fidelity modeling of ocean energy systems. He has collaborated extensively with institutions such as Royal Institute of Technology (KTH) , Lund University , and Technical University of Denmark (DTU) , with funding from agencies including the Swedish Energy Agency and Danish Energy Agency .

• Wave Energy Converters
• Marine Technology
• Computational Fluid Dynamics (CFD)
• +۳ مورد دیگر

Kristin Kirchner is an Assistant Professor at Delft University of Technology in the Delft Institute of Applied Mathematics and holds a concurrent position at KTH Royal Institute of Technology in the Division of Numerical Analysis, Optimization and Systems Theory. Her research centers on Uncertainty Quantification, Stochastic Partial Differential Equations, and Fractional Order Equations, with emphasis on numerical methods for PDEs with random coefficients and applications in computational finance and statistics. She develops computational frameworks for spatiotemporal statistical modeling and functional calculus in high-dimensional settings. Major scientific awards include: ERC Starting Grant for "FunCalc4Stats - Functional Calculus for Computational Statistics" VENI fellowship from NWO for "Efficient spatiotemporal statistical modelling with stochastic PDEs" (2022-2026) Her research is supported by significant European and Dutch funding, with professional profiles maintained on Google Scholar, ORCiD, and ResearchGate.

• Uncertainty Quantification
• Stochastic Partial Differential Equations
• Partial Differential Equations with Random Coefficients
• +۳ مورد دیگر

Sofia Eriksson is an Associate Professor in the Department of Mathematics within the Faculty of Technology at Linnaeus University. She specializes in numerical analysis and scientific computing, with a primary focus on developing finite difference methods for solving time-dependent partial differential equations. Her research has significant applications in fluid dynamics and aerodynamics. Dr. Eriksson's research interests span numerical analysis, scientific computing, finite difference methods, partial differential equations, and computational mathematics. She leads or participates in two major research groups: Computational Mathematics for Predictive Digital Twins (PreDiTwin) and Scientific Computing and Partial Differential Equations. Her work emphasizes developing stable numerical methods with proper boundary and interface treatments. Her publication record shows consistent output since 2007, with 15 recent publications spanning journals like Journal of Computational Physics, SIAM Journal on Numerical Analysis, and Foundations of Computational Mathematics. Her research shows a clear progression from foundational work on finite difference methods to more specialized applications in fluid dynamics and aerodynamics. The publications demonstrate expertise in developing stable numerical schemes, particularly for time-dependent problems with complex boundary conditions. Dr. Eriksson teaches courses in Numerical Methods, Linear Algebra, and Calculus, contributing to both undergraduate and graduate education in mathematics and computational science. Her teaching reflects her research expertise, providing students with practical knowledge of numerical methods applicable to real-world scientific and engineering problems.

• Numerical Analysis
• Scientific Computing
• Finite Difference Methods
• +۴ مورد دیگر