Roger Guimerà (Barcelona, 1976) graduated in Physics at Universitat de Barcelona in 1998, and obtained a PhD in Chemical Engineering from Universitat Rovira i Virgili in 2003. He then moved to Northwestern University where he worked as a postdoctoral fellow and, later, as a Fulbright Scholar. In 2008 he became a Research Assistant Professor at Northwestern's Department of Chemical and Biological Engineering, before accepting his current position at ICREA in 2010. He has been awarded the Premi Nacional de Recerca al Talent Jove (2010), the Erdös-Rényi Prize in Network Science (2012), and the Young Scientist Award for Socio- and Econophysics (2014).
Cells and economies are examples of complex systems. In complex systems, individual components interact with each other giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction's topology, complex systems cannot be properly understood by just analyzing their constituent parts. This feature of complex systems poses important challenges from both a fundamental perspective and an engineering perspective. Roger's research is devoted to the study of complex systems. During his career, he has: (i) made methodological contributions to the study of complex networks, and (ii) used complex network analysis to gain understanding on a number of systems. During the last years, Roger's work has turned to the development of probabilistic models and Bayesian inference, at the interface of probability theory, statistical physics, and interpretable machine learning.
- Fajardo-Fontiveros O, Guimerà R & Sales-Pardo M 2022. 'Node metadata can produce predictability crossovers in network inference problems', Physical Review X,12, 011010.
- Suriyalaksh M, Raimondi C, Mains A, Segonds-Pichon A, Mukhtar S, Murdoch S, Aldunate R, Krueger F, Guimerà R, Andrews S, Sales-Pardo M & Casanueva O 2022. 'Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes', iScience 25(1), 103663.
- Vázquez D, Guimerà R, Sales-Pardo M & Guillén-Gosálbez G 2022. 'Automatic modeling of socio-economic drivers of energy consumption and pollution using Bayesian symbolic regression', Sustainable Production and Consumption, 30, 596-607.
- Gonzalez-Franquesa A et al. 2022, 'Remission of obesity and insulin resistance is not sufficient to restore mitochondrial homeostasis in visceral adipose tissue', Redox Biology, 54, 102353.
- Negri V, Vazquez D, Sales-Pardo M, Guimera R, Guillen-Gosalbez G. 2022, 'Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies', Acs Omega, 7(45), 41147–41164.