I am fascinated with the extraction of knowledge from information, discovering patterns in data and making the most of limited data. This fascination came about during my bachelors education where I can remember a specific moment when I realized that a certain soil formation followed a very predictable pattern, and that I could use this pattern to finish the assigned task much quicker and easier. Ever since, I have been fascinated by the extraction of patterns from data.This fascination has driven me to pursue a Masters education in Computational Geo-ecology. This program focused on bridging the gap between classical Geo-ecological modeling methods and the more mathematical fields such as Machine Learning, Data science, model optimization and, of course, classical dynamical modeling. After this I was invited to a visiting scholar position at the USDA Hydrology and Remote sensing laboratory in the USA. Here, I helped develop a variety of data assimilation methodologies focusing on measurement uncertainties. Next, I started a PhD position at Ghent University, Hydrology and Water management laboratory, to study the uncertainties associated with rainfall observations under varying resolutions. Here, I explored a large variety of different Machine Learning methods such as copulas, regression, dictionary learning, manifold learning, and regression random forests. Generally speaking, my expertise is in statistical modeling and machine learningI am excited to pursue opportunities within the broader field of Machine Learning and Data science, especially if it means my work can make the world a better place. Hobbies: Kick-boxing, cooking and Bread baking, reading, meditation