Abstract:
Since the scientific revolution, interpretable closed-form mathematical models have been instrumenta l for advancing our understanding of the world. Think, for example, of Newton’s law of gravitation, and how it has enabled us to predict astronomical phenomena with great accuracy and to gain deep insights about seemingly unrelated physical phenomena. With the data revolution, we may now be in a position to uncover new mathematical models for many systems,from physics and chemistry to the social sciences. However, to deal with increasing amounts of data, we need approaches that are able to extract these models automatically, without supervision, and with guarantees of asymptotically finding the correct model.
In this talk I will review standard machine learning approaches and discuss their limitations in terms of getting interpretable models. Then, I will present a "machine scientist" that deals rigorously with model plausibilities and also explores systematically the space of models, thus enabling us to obtain closed-form mathematical models from data, and to make out-of-sample predictions that are more accurate than those of standard machine learning approaches.
Bio:
Roger Guimera is ICREA Research Professor of Experimental Sciences and Mathematics at Universitat Rovira i Virgili, in Tarragona, Spain. Roger's research is devoted to the development and application of probabilistic and computational tools for the analysis of complex systems and, particularly, of complex networks. During his career, he has: (i) made methodological contributions to the study of complex networks (e.g. identification of communities and roles, Bayesian network inference, and rigorous model comparison), and (ii) used complex network analysis and network models to gain understanding on specific systems (e.g. social communication and collaboration networks, critical infrastructures such as the air transportation system, and biological systems such as metabolism).
These contributions have won him the Erdos-Renyi Prize of the Network Science Society in 2012, and the Young Scientist Award for Socio- and Econophysics of the German Physical Society in 2014.
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