Projects
Trustworthy and insightful algorithms for industrial decision making KU Leuven
Efficient algorithms for complex personnel scheduling problems are critical for ensuring organisations can provide a suitably qualified workforce at minimal cost while satisfying a wide variety of strict regulations meeting occupational health and safety requirements, as well as employee preferences. This PhD project will focus on nurse rostering, where nurses must be optimally allocated to work shifts in a manner that ensures their ...
Stochastic and Asymptotic Improvements of Scheduling Algorithms University of Antwerp
Trustworthy and insightful algorithms for industrial decision making KU Leuven
Efficient algorithms for complex personnel scheduling problems are critical for ensuring organisations can provide a suitably qualified workforce at minimal cost while satisfying a wide variety of strict regulations meeting occupational health and safety requirements, as well as employee preferences. This PhD project will focus on nurse rostering, where nurses must be optimally allocated to work shifts in a manner that ensures their ...
Trustworthy and insightful algorithms for industrial decision making. KU Leuven
Modeling and Analysis for Efficient Hardware Mapping of Neural Network Algorithms KU Leuven
Machine learning (ML) algorithms are gaining more and more importance nowadays in the area of data processing: data such as images, sound or texts can all be analyzed or improved using ML algorithms. This thesis focuses on the optimization and analysis of hardware design for neural networks, a very popular and efficient ML algorithm.
A first important problem to solve is the off-chip communication, which requires a lot of energy and ...
Structured low-rank matrix / tensor approximation: numerical optimization-based algorithms and applications KU Leuven
Today's information society is centered on the collection of large amounts of data, from which countless applications aim at extracting information. They involve the manipulation of matrices and higher-order tensors, which can be viewed as large multi-way arrays containing numerical data. Key to their successful and efficient processing is the proper exploitation of available structure, and in particular low rank. This project aims to ...
From Virtual Sensing to Executable Digital Twin: Towards Multi-objective, Real-time Estimation Algorithms for Vehicle Dynamics through a Digital Twin Approach KU Leuven
Driven by the digital revolution towards an industry 4.0 context, mechatronic industries such as the automotive sector have undergone major transformations in order to strengthen their flexibility and competitiveness on the market by enhancing vehicle performance and comfort. In addition, safety regulations are becoming increasingly stringent as car accidents happen evermore often due to the rise in population density, especially within large ...
Variations on Component-by-Component Construction Algorithms of Lattice Rules KU Leuven
In the conducted research we develop efficient algorithms for constructing node sets of high-quality quasi-Monte Carlo (QMC) methods which can be used for approximating high-dimensional integrals of multivariate functions. In particular, we study the construction of rank-1 lattice rules and polynomial lattice rules, which are both specified by a generating vector, for numerical integration in weighted function spaces such as Korobov, Sobolev ...