Discrete choice experiments are used in many domains (e.g., transportation, health economics, marketing, environmental economics) to evaluate how product characteristics affect consumer preference. Traditionally, data are modeled using (extensions of) the multinomial logit model. However, researchers have recently started using techniques of the field of machine learning to analyze data of discrete choice experiments. This project aims to ...
Dissecting tissue spatial organization using machine learning and spatial transcriptomics Ghent University
In this project, we aim to better functionally characterize different spatial contexts within tissues. To this end we will develop novel bioinformatics pipelines to process and integrate several U+201ComicsU+201D and imaging data types. Novel machine learning methods will be explored that aim to combine the high spatial resolution of imaging techniques with the deep phenotyping capabilities of current scRNAseq methods.
Explainable machine learning, predictive modelling, and causal inference, applied to medical data Hasselt University
Augmenting policy decisions with machine learning through causal inference and micro-level trace data. Ghent University
In this project, I integrate machine learning (ML) technology and traditional economic frameworks. The explosion of data and computational power are enabling empirical researchers to uncover more detailed insights. While adoption of ML has been widespread, the fields of economics and policymaking have been hesitant because of their focus on causality rather than predictions. Combining prediction power with causal inference, I aim to offer ...
Identification of adaptive mechanisms leading to reduced antibiotic susceptibility in bacterial biofilms using experimental evolution and machine learning approaches Ghent University
Because many mechanisms of reduced sensitivity in bacterial biofilms are still unknown, it is impossible to predict resistance. In this project we will allow bacteria to evolve in vitro in the presence of antibiotics, in order to map all mutations, differences in gene expression and relevant phenotypic characteristics. This will allow to develop a prediction algorithm using machine learning.