Academic Staff

Professor Riccardo Poli Laurea (Florence University), PhD (Florence University)

Position in departmentDirector of Postgraduate Research Studies
Staff positionProfessor
Telephone01206 872338

Member of the Intelligent Systems Research Group

Riccardo Poli is a Professor in CSEE. His main research interests include genetic programming, particle swarm optimisation, the theory of evolutionary algorithms, and brain-computer interfaces. He is a Senior Fellow of The International Society for Genetic and Evolutionary Computation (now ACM SIGEVO) and a recipient of the EvoStar award for outstanding contributions to the field of evolutionary computation. He has published more than 300 refereed papers on evolutionary algorithms, biomedical engineering (more than 60), neural networks and image/signal processing. He was a co-founder of the Essex BCI group in 2004 (the group has now one of the best labs in Europe and has received more funding from EPSRC than all other BCI groups in the UK together). He has co-authored the books Foundations of Genetic Programming, Springer, 2002 and A Field Guide to Genetic Programing, Lulu, 2008. He has been chair of numerous international conferences. He is an advisory board member of the Evolutionary Computation journal, an associate editor of the Genetic Programming and Evolvable Machines journal and a member of the editorial board of Swarm Intelligence. He is an EPSRC Peer-Review-College member and has received funding of over £2.5M at Essex (2001-present). According to Google Scholar he has presently an H-index of 47, meaning that he has 47 publications which have been cited at least 47 times each.


Laurea (equivalent to a BSc plus an MSc) in Electronic Engineering (with Biomedical specialisation) from the University of Florence in Italy, in 1989.

PhD in Biomedical Engineering from the University of Florence, in 1993.

Research interests
  • Evolutionary Computation and Genetic Programming
  • Brain Computer Interfaces
  • Particle swarms
  • Schema Theory
  • Neural Nets
  • Machine Vision, Image and Signal Processing
  • Artificial Intelligence

(See also: staff research interests by category)


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