Imperial College London

Dr Fernando Rosas

Faculty of MedicineDepartment of Brain Sciences

Research Associate
 
 
 
//

Contact

 

f.rosas

 
 
//

Location

 

Electrical EngineeringSouth Kensington Campus

//

Summary

 

Summary

I am a Postdoctoral Researcher at Imperial College London, based at the Centre For Psychedelic Research (Department of Medicine), and also affiliated with the Centre for Complexity Science, the Department of Mathematics, and the Data Science Institute.

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.

Publications

Journals

Azari MM, Rosas F, Pollin S, 2019, Cellular connectivity for UAVs: Network modeling, performance analysis, and design guidelines, Ieee Transactions on Wireless Communications, Vol:18, ISSN:1536-1276, Pages:3366-3381

Rosas De Andraca FE, Faggian M, Ginelli F, et al., Synchronization in time-varying random networks with vanishing connectivity, Scientific Reports, ISSN:2045-2322

Rosas De Andraca F, Chen K-C, Gunduz D, 2018, Social learning for resilient data fusion against data falsification attacks, Computational Social Networks, Vol:5, ISSN:2197-4314

More Publications