The Randstad, Netherlands
I am part of the causal inference group, specifically focusing on formalizing decision analytical models as causal models
Working on the diversity and anti-discrimination project, where we want to quantify and visualize discrimination within Rotterdam per neighborhood
Aiding Prof.Dr.Bas Donkers and Prof.Dr.ir.Benedict Dellaert in researching a risk preference elicitations methods comparison, in a pension context.
I am researching how we can as accurately as possible predict the “Hirschsprung disease” in newborn children by means of using Machine Learning and Deep Neural Networks. It is pioneering in both the medical domain (first attempt ever for the Hirschsprung disease) and in the Data Science domain, since a complete pipeline is being evaluated and optimized. The goal is to use data science as a means to predict the occurrence of the Hirschsprung disease in newborn children in order to be able to provide the required medical help earlier and thus to minimize the risk on lethal complications. Currently, identifying and diagnosing the Hirschsprung disease is medically considered as difficult, evasive and time-consuming. Hence, using and learning from the data uncovers hidden medical patterns, which can aid the medical experts. I am conducting an internship at Emma Children’s Hospital (Amsterdam UMC) and I use their uniquely collected data.
I did my bachelor thesis internship at Infraspeed maintenance BV, focusing on financial forecasting