Nicola Gatti graduated, from Politecnico di Milano, with a M.Sc. degree in Biomedical Engineering in 2001 and with a Ph.D. degree in Information Technology in 2005 (cum laude). Since 2006 to 2015, he was Assistant Professor and, since 2015, he is Associate Professor. He was awarded as the best Italian young researcher on Artificial Intelligence in 2011 by AIxIA and visited the research group of Tuomas Sandholm at Carnegie Mellon University, Pittsburgh, USA, in 2013. Since 2017, he is co-director of the Observatory AI of Politecnico di Milano (together with Alessandro Piva and Giovanni Miragliotta). Since 2018, he is the chair of the Honours Programme in Scientific Research in Information Technology of Politecnico di Milano, and he serves the board of the Ph.D. school. Since 2019, he is board member of the Laboratory CINI AIIS and of the AIxIA. From 2016 to 2022, he is member of the IFAAMAS board.
Nicola Gatti works at the intersection of Computer Science, Microeconomics, Optimization, and Machine Learning since 2006. His main works are on algorithmic game theory (negotiation, security games, equilibrium computation), algorithmic mechanism design (sponsored search auctions and online advertising), algorithmic social choice (election manipulation), and online learning (regret minimization in equilibrium and economic problems). He is currently one of the most prolific researchers on the premier artificial intelligence venues among those working in Italy .
PRIN2017 Algadimar, PI of the unit of Politecnico di Milano (kickoff).
Combinatorial optimization/machine learning tools for automatic pricing: lastminute.com, DoveVivo.
Combinatorial optimization/machine learning tools for automatic advertising: MMM group, AdsHotel.
Combinatorial optimization/machine learning tools for military defense: Analisi e Valore, Italian Navy.
The course presents scheduling real-time algorithms, computer input/output, software engineering groundigs. Additional material is available on BeeP.
The course presents algorithmic game theory and mechanism desing topics. Additional material is available on BeeP.
The course presents combinatorial online learning techniques and their application to real-world scenarios Additional material is available on BeeP.
The course presents basic online learning algorithms and scenarios for companies and industries. See here.
The course presents algorithms to solve huge sequential noncooperative games.
Researchers, Postdoctoral students, Doctoral students, Honours Programme students