Bodossaki Distinguished Young Scientist Awards 2023
Applied Science: Applications of Artificial Intelligence Methods and Technologies in Engineering Disciplines
«The Bodossaki Distinguished Young Scientist Award is of great importance to me and one of the greatest honors I have received in my career. An award that has been given in the past to scientists that have been a role model and inspiration for me throughout my career. It is also a great honor to be selected, knowing the many active brilliant young Greek researchers in the field of artificial intelligence. I would like to deeply thank Bodossaki Foundation for maintaining the tradition of these awards throughout the years.»
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Vasilis Syrgkanis is Assistant Professor in the Department of Management Science and Engineering and (by courtesy) in the Department of Computer Science at Stanford University. His research lies in the fields of artificial intelligence and machine learning, game theory and theory of algorithms, with ultimate goal the development of novel techniques for datadriven decision making.
He was born in Thessaloniki in 1986 and at the age of 10 he moved to Volos, and graduated from the 2nd Lykeio of Volos in 2004. In 2009 he completed his studies at the National Technical University of Athens, in the School of Electrical and Computer Engineering. During his undergraduate studies, he completed his
diploma thesis on algorithmic game theory, under the supervision of Professor E. Zachos.
In August 2009 he moved to the US, for his PhD studies under the supervision of Professor E. Tardos, in the Department of Computer Science at Cornell University. For his PhD studies he was awarded the Simons Foundation Fellowship. His PhD work developed a new mathematical theory for the analysis of efficiency in complex economic systems, with primary applications in electronic markets. After finishing his PhD studies in 2014, he worked as a post-doctoral researcher for two years in the Microsoft Research New York City lab. In 2016 he moved to Boston, where he started working as a Researcher and then Principal Researcher in the Microsoft Research, New England lab, until August 2022. During his time at the lab, he co-founded and co-led the Automated Learning and Intelligence for Causation and Economics (ALICE) team. His research focused on developing new methods for causal inference using machine learning. His work was used by Microsoft and other companies in the tech sector, to analyze the efficacy of policies in operations management and to optimize electronic platforms. Moreover, with the ALICE team, he developed the open-source software EconML, which is widely used in the industry practice of causal machine learning.
In September 2022, he was elected Assistant Professor in the Department of Management Science and Engineering at Stanford University. His research team
at Stanford works on topics in artificial intelligence and machine learning, with primary focus the development of novel causal inference and data driven decision
making techniques. The primary application domains of his research are biomedical science, operations management and digital experimentation. His work has received several best paper awards at leading computer science conferences (2015 ACM Conference on Economics and Computation, 2015 Annual Conference on Neural Information Processing Systems, 2019 Conference on Learning Theory). His recent research efforts at Stanford received a 2023 Amazon Research Award. He lives at Stanford, with his wife and their two daughters.
