Imperial College London


Business School

Advanced Research Fellow - Data Scientist



s.cenci Website




Business School BuildingSouth Kensington Campus





Simone Cenci is an Advanced Research Fellow and Data Scientist at the Leonardo Centre on Business for Society at Imperial College Business School.

His research focuses on statistical and causal inference from observational data. The overarching goal of his work is to build empirical models that shed new light on the underlying causal mechanisms that link companies' behavior, their financial performance, and their societal impact. In this context, he is currently working on developing a comprehensive dataset, as well as a set of empirical tools, to characterize firms' sustainability behavior, from their attitude towards stakeholders to the impact that different types of sustainability strategies have on their financial performance. 

Before joining Imperial College, Simone was a postdoctoral research associate at DCI, LLC, an independent asset management firm now acquired by Blackstone Credit. At DCI, he worked on statistical and causal inference methods to explain and predict firms' capital structure dynamics. Simone obtained his Ph.D. at the Massachusetts Institute of Technology, working on nonparametric statistical inference from nonlinear time series data.

Selected Publications

Journal Articles

Cenci S, Medeiros LP, Sugihara G, et al., 2020, Assessing the predictability of nonlinear dynamics under smooth parameter changes, Journal of the Royal Society Interface, Vol:17, ISSN:1742-5689

Cenci S, Saavedra S, 2019, Non-parametric estimation of the structural stability of non-equilibrium community dynamics, Nature Ecology & Evolution, Vol:3, ISSN:2397-334X, Pages:912-918

Cenci S, Sugihara G, Saavedra S, 2019, Regularized S-map for inference and forecasting with noisy ecological time series, Methods in Ecology and Evolution, Vol:10, ISSN:2041-210X, Pages:650-660

Cenci S, Saavedra S, 2018, Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data, Journal of the Royal Society Interface, Vol:15, ISSN:1742-5689

Cenci S, Montero-Castano A, Saavedra S, 2018, Estimating the effect of the reorganization of interactions on the adaptability of species to changing environments, Journal of Theoretical Biology, Vol:437, ISSN:0022-5193, Pages:115-125

Cenci S, Saavedra S, 2018, Structural stability of nonlinear population dynamics, Physical Review E, Vol:97, ISSN:2470-0045

Cenci S, Song C, Saavedra S, 2017, Rethinking the importance of the structure of ecological networks under an environment-dependent framework

Saavedra S, Cenci S, del-Val E, et al., 2017, Reorganization of interaction networks modulates the persistence of species in late successional stages, Journal of Animal Ecology, Vol:86, ISSN:0021-8790, Pages:1136-1146

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