ICREA Academia
Jordi Vallverdú Segura

Jordi Vallverdú Segura

ICREA Acadèmia 2019

Universitat Autònoma de Barcelona · Humanities

Jordi Vallverdú Segura

Jordi Vallverdú, Ph.D., M.Sci., B.Mus, B.Phil  is a Catalan investigator who has devoted his researches to the cognitive and epistemic aspects of Philosophy of Computing, Philosophy of Sciences, Cognition, and Philosophy of AI. After his Bachelor in Philosophy at U. Barcelona (1996), he obtained his M.Sci and Ph.D. at U. Autònoma de Barcelona (2001, 2002, respectively). He also obtained his Bachelor in Music (ESMUC, 2011). He has had research stays at Glaxo-Wellcome Institute for the History of Medicine (1997), J.F.K. School at Harvard University (2000), and Nishidalab at Kyoto University (2011, under a JSPS Grant). He has been visiting professor at Technische Universität München, and the University of Reading, and has been invited by a large number of institutions to lecture and to teach specialized seminars. In 2019 won the Best presentation award of the HUAWEI Neuro-inspired, cognitive, and unconventional computing workshop, Kazan (Russia).




Research interests

Professor Vallverdú researches have been exploring two main related areas: epistemology and cognition. Since his early Ph.D. research on epistemic controversies, Prof. Vallverdú has analyzed several aspects of computational epistemology.

His latest researches have been focused on the Causal challenges of  Machine Learning techniques, with special emphasis in Deep Learning. As one of the most promising advances, Statistics meets Causal graph reasoning (via Directed Acyclic Graphs), with several conceptual paths still to be explored and clearly identified. Counterfactual reasoning is a fundamental part of these open debates, which are under the analysis of Prof. Vallverdú.


His current researches are done under the support of these projects: GEHUCT, Recercaixa AppPhil Project, Tecnocog (MICINN funded project headed by Prof. Vallverdú), and H2020-SwafS  CSI COP European Project.









Epistemology, Deep Learning, Science,Statistics, Causality, Cognition, Heuristics