معرفی
Dirk Nuyens is a Senior Lecturer in the Department of Computer Science at the Faculty of Engineering Sciences, KU Leuven, Belgium. He is affiliated with the Numerical Analysis and Applied Mathematics (NUMA) research unit and is a member of the iSi Health Institute for Physics-based Modeling for In Silico Health. He also serves on the Faculty Council of Engineering Sciences and the Programme Committees for Engineering Sciences and Computer Science.
Education: While specific degrees are not listed, his expertise and research output indicate advanced training in numerical analysis, computational mathematics, and applied mathematics.
Research Interests: His primary research focuses on numerical analysis, computational mathematics, and quasi-Monte Carlo (QMC) methods. Specific areas include high-dimensional integration and approximation, lattice rules, low-discrepancy sequences, uncertainty propagation, and financial engineering. He develops algorithms for efficient sampling, integration, and approximation in high-dimensional spaces, with applications in uncertainty quantification, Bayesian inversion, and PDEs with random coefficients.
Research Trends: His recent publications emphasize the construction and analysis of embedded lattice-based algorithms for multivariate function approximation and integration. There is a strong focus on achieving higher-order convergence rates, particularly in Korobov and Sobolev spaces, and on developing randomized lattice rules for improved error bounds. His work bridges theoretical developments in numerical analysis with practical implementations in scientific computing and engineering.
Scientific Contributions: He has authored or co-authored numerous influential papers in top-tier journals such as Mathematics of Computation, Journal of Complexity, SIAM Journal on Numerical Analysis, and Journal of Computational Physics. His work on lattice rules, QMC methods, and high-dimensional integration has been widely cited and used in fields ranging from computational finance to theoretical physics.
Projects and Funding: He leads or co-leads several ongoing and completed research projects funded by national and international agencies. These include projects on turbulence reconstruction from partial observations, uncertainty quantification for climate control in buildings, computational methods for infinite-dimensional Bayesian inversion, circularity improvements in scrap analysis, and ML-based sensitivity analysis.
Software Development: He maintains and contributes to open-source software repositories for QMC point generation, including the Magic Point Shop and QMC4PDE projects. These provide efficient implementations of lattice and digital sequence generators in MATLAB, C++, and Python.
Institutional Service: Beyond research, he contributes to academic governance through his roles in faculty and departmental councils, influencing curriculum development and strategic planning in engineering and computer science education at KU Leuven.
