In the Translational Data Science LAB of Prof. dr. Marco Spruit at the transdisciplinary Health Campus The Hague, we connect practical problems in healthcare practices (LUMC) to fundamental challenges in data science (LIACS) to bridge the best of both worlds! Good things come in threes, therefore, our Translational Data Science for Population Health research theme is also structured into three research lines. The first is Data Engineering where we work on Federated Machine Learning for decentralised trust networks, Natural Language Processing for wellbeing chatbots, and synthetic data generation towards establishing a digital twin of our ELAN population datawarehouse. The second research line is Data Analytics where we investigate Automated Machine Learning to allow physicians to perform their own data analyses on their own patients data in a responsible manner, and Explainable AI for gender bias mitigation in ML models. The third and final TDS research line is eHealth Implementation where we focus on AI Implementation Science and Machine Learning Operations to manage concept/data drift. Through this Translational Data Science approach, we aim to achieve a better fundamental understanding of the world around us by being societally inspired, demand-driven and solution-oriented.