Research. Lorenzo

Lorenzo Magnino

I'm Lorenzo Magnino, a researcher at University of Cambridge, supervised by Amanda Prorok.

My work now explores the intersection of Multi-Agent Robotics and Collective Intelligence. During my time at NYUShanghai I worked on Mean Field Games and Reinforcement Learning supervised by Mathieu Lauriere. I also interned at InstaDeep, working on GNNs for Multi-Agent Reinforcement Learning.

I love science, building things, playing music, and sports.

Research: World Models · Robot Learning · Multi-Agent Systems

Feel free to write me on LinkedIn or connect directly by email.

lm2183[at]cam[dot]ac[dot]uk

LinkedIn Profile

GitHub Profile

Google Scholar

Collaborated with: University of Cambridge, NYU, UCLA, Earth Science Projects, KTH Royal Institute of Technology, Georgia Institute of Technology.

Publications Repositories Experiences Projects Music

Snapshots from recent Symposium on "Future of Intelligent Robotics" in Cambridge.

News

Jan 2026: Starting as TA for Robot Mobile Systems, course taught by Amanda Prorok, Cambridge University
Jan 2026: ProbAI Winter School. Diffusion Models and Optimal Transport
Dec 2025: Poster presentation at NeurIPS 2025 in San Diego
Nov 2025: Talk at Symposium "Future of Intelligent Robotics" in Cambridge
Oct 2025: Starting as a Research Assistant at the University of Cambridge, ProrokLab
Jul 2025: Poster presentation at ICML 2025 in Vancouver
Mar 2025: Intern at InstaDeep (MARL and GNN for routing and scheduling in a real world challenge)
Sep 2024: Continue as Research Assistant at NYU Shanghai w. Mathieu Lauriere
Mar 2024: Head to NYU Shanghai as a visiting student

Featured Publications

Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games

Lorenzo Magnino, Jiacheng Shen, Matthieu Geist, Olivier Pietquin, Mathieu Laurière

(submitted, pre-print soon)

Paper Code

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

Lorenzo Magnino, Kai Shao, Zida Wu, Jiacheng Shen, Mathieu Laurière

NeurIPS 2025

Paper Poster Code

Learning to Stop: Deep Learning for Mean Field Optimal Stopping

Lorenzo Magnino, Yuchen Zhu, Mathieu Laurière

ICML 2025

Paper Website Code

Cool things on World Models coming soon

Visit the Lab

Talks

Prob_AI Winter School: Mathematical Foundations of Probabilistic AI Warwick University, Jan 2026
NeurIPS 25: poster presentation San Diego, Dec 2025
Cambridge CST Symposium: Foundation models in robotics Madingley Hall, Cambridge UK, Nov 2025
Insta Deep AI week - Workshop: Graph Neural Network for Multi-Agent Routing Problem Berlin, Jul 2025
ICML 25: poster presentation Vancouver, Jul 2025
Seminar: “Latest advances in Dynamic Programming in Wasserstein spaces” with Professor M. Fischer Padova, Aug. 2023
Research internship with Professor M. Formentin Padova, Apr. 2022 – Jul 2022

High-dimensional Probability for Statistical Learning (using Dudley's inequality and covering numbers to derive an upper bound for the excess risk in machine learning theory)

Projects

RoboML Research Template

A lightweight, opinionated template for running machine learning and robotics research projects. It provides a standardized structure for experiments, data handling, logging, and evaluation, with ready-to-use configs and scripts for reproducible workflows.

ML/Robotics Community

GitHub

★ Star and clone the repo!

...some interesing readings

The Creative Act: A Way of being by Rick Rubin
The art of doing Science and Engineering by R. Hamming
What the Tortoise Said to Achilles by L. Carroll
Why Greatness cannot be Planned by K. Stanley and J. Lehman