I am the lead scientist of a 50-people tech org at Amazon, and the science manager of its ML team. My team and I work on a wide area of topics (e.g. forecasting, ranking, causal inference, A/B testing, or recommendations systems) in order to: i) drive and capturing customer engagement, ii) grow the pool of Amazon customers, and iii) increase the acquisiton of Prime customers.
In addition to my job in forecasting research, I carry out research and I co-supervise a PhD student in the area of AI and the energy transition, another research topic that I am interested in.
I hold a PhD degree from TU Delft in that same topic, i.e. AI applied to the energy transition, and my PhD research on this domain has received several awards.
I am the developer of optidef, a Latex library for defining optimization problems, and of the epftoolbox python library, the first open-access library for driving research in electricity price forecasting.
Lead scientist of a 50-people tech team (tech arm of the EU Prime & Marketing org), and science manager of its ML team. My work as a manager requires 50% of my time. The remaining 50% I act as a science lead in multiple projects: i) measuring customer engagement; ii) evaluating impact of Youtube campaigns; iii) recommending discounts to drive customer engagement; iv) evaluating downstream valuation of discounts via causal inference.
Applied Scientist at Amazon working on forecasting and ranking algorithms.
Research scientist in AI techniques to facilitate the energy transition and the integration of renewable sources.
Research assistant in the area of optimization and optimal control. Teaching assistant in two M.Sc. courses.
PhD in data science in the context of energy systems and energy markets with the end goal of advancing the energy transition through AI.
Masters degree in Microsystems engineering with a major in optimization and optimal control.
Five-year bachelor degree in Electronics, Automatic Control and Robotics