Summary
Alessio Quaglino is a Senior Research Engineer at DeepMind with a PhD in Mathematics and a strong track record building physics-driven simulation libraries and ML systems for industry. He blends rigorous mathematical modeling and uncertainty quantification with practical engineering—having developed MuJoCo components, optimized Formula 1 suspension models at McLaren, and shipped a SaaS deep-learning controller for chemical processes. Alessio moves fluidly between C++ performance engineering (collision and FE solvers) and Python research tooling for large-scale simulations, and has led and mentored early-career researchers while administering graduate programs. His work sits at the intersection of simulation, optimization, and data-driven automation, with the uncommon combination of hands-on race-car dynamics experience and formal UQ expertise. Based in London, he brings both academic depth and product-focused delivery to complex, real-time physical systems.
4 years of coding experience
10 years of employment as a software developer
KTH Royal Institute of Technology
Bachelor of Science (B.Sc.), Engineering Mathematics, Bachelor of Science (B.Sc.), Engineering Mathematics at Politecnico di Milano
Doctor of Philosophy (Ph.D.), Mathematics, magna cum laude, Doctor of Philosophy (Ph.D.), Mathematics, magna cum laude at Georg-August-Universität Göttingen
English, Italian, Swedish