After graduating with a degree in Mathematics, Pascal worked for five years as Bioinformatician for the CHUL Research Center in Québec City. He first gained expertise in the genetics of bipolar disorder and then worked on software and model development for gene expression response to steroids in breast and prostate cancer in mouse models. His deep interest in computational biology made him start graduate studies. As a graduate student, he developed expertise in the genetics of modifier genes in Glaucoma and in Monte-Carlo Markov Chain (MCMC) analysis. During his first postdoctoral position, he contributed to the development of a reverse jump MCMC method for localization of nucleosomes, C++ code optimization, and method development related to methylation inheritance. Since joining the Tuveson and Krasnitz Laboratories, Pascal has been working on machine learning methods for prediction of drug responses based on genomic information in pancreatic cancer. Pascal’s main interest is to model genomic information related to pancreatic cancer in the objective of gaining a better understanding of the disease.