Giorgia Ramponi

I have an appointment as Assistant Professor with Tenure Track in Artificial Intelligence for Cyber-Physical Systems at the Faculty of Business, Economics and Informatics at the University of Zurich. Previously, I was a postdoctoral researcher at ETH AI Center and sponsored by Google Brain. At ETH I am advised by Niao He and Andreas Krause. My research interests lie in machine learning and mathematical modelling, with a focus on reinforcement learning and multiagent learning.

In June 2021, I completed my Ph.D. in Information Technology at Politecnico di Milano (with honors) advised by Marcello Restelli. In July 2017, I obtained a Master of Science in Computer Science with the Honours Programme (110/110 cum laude) at la Sapienza advised by Flavio Chierichetti and Alessandro Panconesi.

In my previous life I worked on Social Network Analysis with Marco Brambilla and Stefano Ceri, and on Networking with Gaia Maselli.

Email  /  CV  /  Google Scholar  /  Github

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  • I am happy to be part of the ELLIS community.
  • Our paper On Imitation in Mean-field Games" has been accepted at NeurIPS 2023!
  • I was invited as lecturer at the Mediterranean Machine Learning Summer School to talk about Deep Reinforcement Learning.
  • I gave a talk on Reinforcement Learning and Multi-agent Learning at the New Frontiers in Learning, Control, and Dynamical Systems workshop at ICML 2023. See everyone in Hawaii!
  • Four papers accepted at EWRL 2023!
  • Designing and teaching a new course called Data Science and Machine Learning for the ETH-Ashesi Master program.
  • Two papers accepted at NeurIPS 2022! "Active Exploration for Inverse Reinforcement Learning" and "Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions".
  • Our open problem "Do you pay for Privacy in Online learning?" has been selected at COLT 2022.
  • Our paper "Active Exploration for Inverse Reinforcement Learning" has been accepted at the Adaptive Experimental Design and Active Learning in the Real World (ReALML) workshop at ICML 2022.
  • Our paper "Learning in Markov Games: can we exploit a general-sum opponent?" has been accepted as oral (~4%) at UAI 2022.
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Thanks Jon Barron for this nice template.