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Gabriele Farina is a computer scientist who studies machine learning, optimization and game theory, especially decision-making with incomplete information. His research connects individual learning rules with the collective behaviour of interacting decision-makers.^[ref]

One focus is no-regret learning: can repeated local adaptation lead to a useful game-theoretic equilibrium? The computational question matters because a multi-agent system must find strategies through available observations and calculations. His doctoral work studied learning and equilibrium computation in imperfect-information games.^[ref]

Homepage: www.eecs.mit.edu/people/gabriele-farina/