My research interests are in optimization, machine learning and decision making. I have worked (and still work) with many wonderful colleagues and research students, and in the area of multiple objective optimization, we've made contributions to local search, memetic algorithms, hypervolume maximization, archiving, multiobjective machine learning, No Free Lunch theorems, and surrogate-assisted or Bayesian optimization methods. We've also worked on applied problems from science and industry, especially in biology, medicine and physics (and, at Invenia, electricity grids). Some of our research has applied optimization and learning in online (or "closed-loop") settings, with real experiments (or the real-world) in the loop, and we have introduced techniques based on reinforcement learning to handle the dynamic constraints and other interruptions that arise in this context.