Gabriele Farina grew up in a small town in a hilly winemaking region of northern Italy. Neither of his parents had college degrees, and although each were convinced they “didn’t understand math,” Farina says, they bought him the technical books he wanted and didn’t discourage him from attending the science-oriented, moderately than the classical, highschool.
By around age 14, Farina had focused on an idea that might prove foundational to his profession.
“I used to be fascinated very early by the concept a machine could make predictions or decisions so significantly better than humans,” he says. “The proven fact that human-made mathematics and algorithms could create systems that, in some sense, outperform their creators, all while constructing on easy constructing blocks, has at all times been a significant source of awe for me.”
At age 16, Farina wrote code to resolve a board game he played together with his 13-year-old sister.
“I used game after game to compute the optimal move and prove to my sister that she had already lost long before either of us could see it ourselves,” Farina says, adding that his sister was less enthralled together with his recent system.
Now an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a principal investigator on the Laboratory for Information and Decision Systems (LIDS), Farina combines concepts from game theory with such tools as machine learning, optimization, and statistics to advance theoretical and algorithmic foundations for decision-making.
Enrolling at Politecnico di Milano for school, Farina studied automation and control engineering. Over time, nonetheless, he realized that what activated his interest was not “just applying known techniques, but understanding and increasing their foundations,” he says. “I steadily shifted increasingly more toward theory, while still caring deeply about demonstrating concrete applications of that theory.”
Farina’s advisor at Politecnico di Milano, Nicola Gatti, professor and researcher in computer science and engineering, introduced Farina to research questions in computational game theory and encouraged him to use for a PhD. On the time, being the primary in his immediate family to earn a school degree and living in Italy, where doctoral degrees are handled in another way, Farina says he didn’t even know what a PhD was.
Nevertheless, one month after graduating together with his undergraduate degree, Farina began a doctoral degree in computer science at Carnegie Mellon University. There, he won distinctions for his research and dissertation, in addition to a Facebook Fellowship in Economics and Computation.
As he was ending his doctorate, Farina worked for a yr as a research scientist in Meta’s Fundamental AI Research Labs. One in all his major projects was helping to develop Cicero, an AI that was in a position to beat human players in a game that involves forming alliances, negotiating, and detecting when other players are bluffing.
Farina says, “once we built Cicero, we designed it in order that it might not conform to form an alliance if it was not in its interest, and it likewise understood whether a player was likely lying, because for them to do as they proposed can be against their very own incentives.”
A 2022 article within the MIT Technology Review said Cicero could represent advancement toward AIs that may solve complex problems requiring compromise.
After his yr at Meta, Farina joined the MIT faculty. In 2025, he was distinguished with the National Science Foundation CAREER Award. His work — based on game theory and its mathematical language describing what happens when different parties have different objectives, after which quantifying the “equilibrium” where nobody has a reason to vary their strategy — goals to simplify massive, complex real-world scenarios where calculating such an equilibrium could take a billion years.
“I research how we will use optimization and algorithms to really find these stable points efficiently,” he says. “Our work tries to shed recent light on the mathematical underpinnings of the idea, higher control and predict these complex dynamical systems, and uses these ideas to compute good solutions to large multi-agent interactions.”
Farina is very desirous about settings with “imperfect information,” which implies that some agents have information that’s unknown to other participants. In such scenarios, information has value, and participants should be strategic about acting on the knowledge they possess in order not to disclose it and reduce its value. An on a regular basis example occurs in the sport of poker, where players bluff with a purpose to conceal details about their cards.
In line with Farina, “we now live in a world by which machines are much better at bluffing than humans.”
A situation with “massive amounts of imperfect information,” has brought Farina back to his board-game beginnings. Stratego is a military strategy game that has inspired research efforts costing thousands and thousands of dollars to provide systems able to beating human players. Requiring complex risk calculation and misdirection, or bluffing, it was possibly the one classical game for which major efforts had failed to provide superhuman performance, Farina says.
With recent algorithms and training costing lower than $10,000, moderately than thousands and thousands, Farina and his research team were in a position to beat the very best player of all time — with 15 wins, 4 draws, and one loss. Farina says he’s thrilled to have produced such results so economically, and he hopes “these recent techniques might be incorporated into future pipelines,” he says.
“We have now seen constant progress towards constructing algorithms that may reason strategically and make sound decisions despite large motion spaces or imperfect information. I’m enthusiastic about seeing these algorithms incorporated into the broader AI revolution that’s happening around us.”