Projects
Deep Process Model Forecasting KU Leuven
Understanding High-Dimensional Time-Series: Topological and Visual Analytics for Characterizing Sleep Apnea. Hasselt University
Building for Belgium: Belgian Embassies in a Globalising World (1945-2020) KU Leuven
In recent decades, architectural historians have increasingly scrutinised the embassy building program of state actors. Framing an embassy to be the physical manifestation of the state, scholars have examined if and to what extent state actors have approached the commissioning of a purpose-built embassy as an opportunity to radiate nationhood abroad and express foreign policy stands and, more importantly, what kind of architecture they saw ...
Asymptotics for Orthogonal Polynomials and High-frequency Scattering Problems KU Leuven
The goal of this thesis is to exploit asymptotic behaviour in high-degree orthogonal polynomials and high-frequency acoustic scattering problems to obtain %[Dave] problems. This leads to
a lower computational cost. The code is made publicly available and validates this goal as well as the accuracy of the results of this thesis.
We are interested in the higher-order asymptotic behavior of orthogonal polynomials of Jacobi-, ...
PARADISE: Pushing AsteRoseismology to the next level withTESS, GaiA and the Sloan DIgital Sky SurvEy KU Leuven
Real-time pathogen phylodynamics KU Leuven
With high-throughput molecular sequencing, genomic data from pathogenic viruses are becoming available in unprecedented quantities and with remarkable speed, even in in resource-limited settings, aided by portable genome sequencing technology. However, the wealth of sequence data for most important infectious diseases is stretching current computational approaches, such as phylogenetic inference, to their practical limits. By developing and ...
Brand placement effectiveness: Towards an integrative framework. University of Antwerp
Improving the Interpretability, Bias, and Fairness of Process-Driven Decision Models KU Leuven
The avalanche of data-driven solutions nowadays is overwhelming. While data fuels the advances in machine learning and artificial intelligence, these areas have mostly focused on the computational aspects while often neglecting the interpretation, actionability, and implications of their results. Furthermore, the data as well as the algorithms used to obtain models of the data's provenance are often biased. These biases can lead to ...