Alvaro Egea
Technology Specialist Policy Officer (EU AI Office) at European Commission
London, England, Luxembourg
Summary
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Senior🎓
Top SchoolAlvaro Egea is a technology specialist and policy officer at the EU AI Office with eight years’ experience applying reinforcement learning, time series analysis, and AI ethics to real-world mobility and governance problems. He holds a PhD in Applied Mathematics and has moved fluidly between research roles at The Alan Turing Institute and industry positions—most recently leading GenAI and AI ethics work at Fujitsu Research, managing a distributed team and delivering PoCs on LLMs and agents. His background includes hands-on RL deployments for traffic signal control with Vivacity Labs and collaborative urban-traffic research with the Toyota Mobility Foundation, giving him rare end-to-end experience from algorithm design to fielded systems. Comfortable translating technical risk into policy and compliance inputs (e.g., for the EU AI Act), he blends rigorous quantitative skills with practical product and partnership building. An uncommon thread in his career is the shift from aerospace stress engineering to cutting-edge AI, reflecting a strong systems mindset and appetite for complex, interdisciplinary problems.
8 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics for Real-World Systems (Applied Mathematics and Statistics), Doctor of Philosophy (Ph.D.) Mathematics for Real-World Systems (Applied Mathematics and Statistics) at University of WarwickEnrichment Scheme Researcher Reinforcement Learning, Enrichment Scheme Researcher Reinforcement Learning at The Alan Turing InstituteBachelor of Science (BSc) Aerospace Aeronautical and Astronautical/Space Engineering, Bachelor of Science (BSc) Aerospace Aeronautical and Astronautical/Space Engineering at Universidad Politécnica de Madrid
Github Skills (3)
reinforcement-learning10
dqn10
testing1
Programming languages (2)
Github contributions (5)
ACabrejas/IEEE_ITSC2020_WARP
Mar 2019 - Jan 2021
Contributions:56 commits, 44 pushes, 3 comments in 1 year 10 months
ACabrejas/Vissim_RL_Traffic_Lights_Control
Jul 2018 - Sep 2021
VISSIM Reinforcement Learning for Urban Traffic Control. DQN, DDQN, D3QN with Target Networks, A2C, PER.
Contributions:198 commits, 74 pushes, 1 comment in 3 years 3 months
dqnreinforcement-learning
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