MIT - Graduate Research Assistant

• Conducting fully-funded research on Reinforcement Learning and Mathematical Programming algorithms for Dynamic Resource Management in satellite mega-constellations
• Initiated and now leading a sponsored 2-year-long research project on Reinforcement Learning for drug discovery and molecular optimization
• Mentored 10 undergraduate students and directly supervised 4 undergraduate Thesis
• Participated in 9 conferences and workshops
• Every semester I take on a side project connected with Reinforcement Learning or other state-of-the-art Machine Learning areas (e.g., Graph Neural Networks, Meta Learning)

MIT - Graduate Lead

• Leading a team of 4 graduate students working on the design of the next generation of Resource Allocation and Revenue Management systems for highly-flexible mega-constellations
• This project is sponsored by SES: our team reports monthly to SES engineers and holds a semiannual review with the CTO and other senior managers
• Every year I co-lead the organization of a knowledge transfer workshop with engineers at SES HQ in Luxembourg

Google Brain - Student Researcher

• Applied MetaPG, the framework developed during my internship, to a use case on evolving RL algorithms that optimize and trade high returns, generalizability, and stability
• Our method was able to find RL algorithms that improve upon SAC's performance and generalizability by 3% and 17%, respectively, and reduce instability up to 65%
• Actively communicated with research scientists among the Reinforcement Learning, AutoML, and Robotics teams at Google Brain
• Routinely presented my work to larger audiences in seminars or group meetings across Google Brain

Google Brain - Research Intern

• Designed, implemented, and tested MetaPG: a multiobjective Meta Evolution framework to optimize multiple Reinforcement Learning goals simultaneously and gain insights on underlying tradeoffs
• MetaPG provides a proof-of-concept for bridging the gap between the design of RL algorithms and the needs of RL practitioners

Novartis AI Innovation Lab - Machine Learning Engineer Intern

• Developed Deep Reinforcement Learning models for molecular design and molecular property optimization, achieving better performance than state-of-the-art models on a subset of tests from the Guacamol benchmark suite
• The project was framed inside a collaboration between Novartis and Microsoft Research, I routinely presented my work to researchers in both companies

ARCVI - Data Scientist

• Developed injury risk prediction models that outperformed previous methods' accuracy by 20% (impact of 90-170k€)
• Defined a new disposal architecture for 1,500 vending machines by means of Mathematical Programming, with an estimated impact of 380k€
• Developed consumption likelihood models for retail vendors and designed commercial strategies using behavioral economics principles
• Co-authored the business report Adoption and Impact of Big Data and Advanced Analytics in Spain [Spanish version]

Barcelona Supercomputing Center - Undergraduate Research Assistant

• Adapted different Machine Learning models for large-scale multiprocessing architectures
• After testing on the MareNostrum 4 supercomputer, our algorithms resulted in a 10x speed increase for high-dimensional clustering tasks