Imperial College London

Dr Fernando Rosas

Faculty of Natural SciencesDepartment of Mathematics

Marie Skłodowska-Curie Individual Fellowship







Electrical EngineeringSouth Kensington Campus





I am a Marie-Słodowska Curie Fellow at Imperial College London, based at the Centre of Complexity Science and affiliated both in the Departments of Mathematics and the Department of Electrical and Electronic Engineering.

My current work is focused in the development of tools to enable a deeper understanding of the interdependencies that can take place in systems composed of many interacting agents. I am interested in the most fundamental and theoretical aspects of this problem, and also in the consequences and applications in diverse contexts, related to basic sciences, engineering and arts.

Although we currently live in a world characterized by interrelationship and connectivity, the interdependencies that can exist between three or more variables or stochastic processes are poorly understood. Improving this understanding is paramount for future advances in neuroscience, genetics, network science and many other fields that explore systems composed by many interacting variables. Moreover, this understanding might prove to be fundamental in our post-modern society, where the surprising outcomes of recent political polls (e.g. the Brexit referendum and the latest US presidential election) are revealing the limitation of our current understanding of social behaviour in a highly-interconnected world.



Rosas De Andraca F, Chen K-C, Gunduz D, Social learning for resilient data fusion against data falsification attacks, Computational Social Networks

Dolan D, Jensen HJ, Mediano PAM, et al., 2018, The Improvisational State of Mind: A Multidisciplinary Study of an Improvisatory Approach to Classical Music Repertoire Performance, Frontiers in Psychology, Vol:9, ISSN:1664-1078


Rosas De Andraca F, Azari MM, Murillo Y, et al., Coverage Maximization for a Poisson Field of Drone Cells, 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017)

Rosas De Andraca F, Gunduz D, Chen K-C, Social Learning Against Data Falsification in Sensor Networks, Conference on Complex Networks

Rosas De Andraca F, Manolakis K, Oberli C, et al., Impact of the interference correlation on the decoding error statistics, 51th Asilomar Conference on Signals, Systems, and Computers

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