Professor Eleni Vasilaki

Chair of Computational Neuroscience & Neural Engineering
Director or Research, Head of Machine Learning Group.

Room number: 146 Regent Court
Telephone: +44 (0) 114 222 1822

Head of the Machine Learning research group and member of the Complex Systems Modelling research group


Selected publications | All publications

Prof. Eleni Vasilaki



Eleni graduated with a Bachelor’s degree in Informatics and Telecommunications and a Master’s degree in Microelectronics from the University of Athens, before taking her DPhil (PhD) at Sussex, in Computer Science and Artificial Intelligence. From 2004 to 2006 she worked at the University of Bern and from 2007 to 2009 at the Swiss Federal Institute of Technology Lausanne (EPFL). In 2009 she joined the University of Sheffield as Lecturer, where she is Senior Lecturer from 2013.

Other professional activities and achievements

  • Academic Editor for PLOS ONE, PeerJ, Scientific Reports.
  • Director for the Organisation for Computational Neurosciences (2016-2019)
  • Programme Committee member of ICANN 2013, 2014 and PCI 2013.
  • Area Chair for key machine learning conference NIPS 2014.
  • Reviewer for several journals and key conferences, including Nature Nanotechnology, Neural Networks, Journal of Physiology Paris, Biological Cybernetics, Frontiers in NeuroEngineering, Frontiers in NeuroRobotics, Computational Neuroscience, PLOS ONE, International Journal of Neural Systems, Computational Neuroscience (CNS), The International Conference on Artificial Neural Networks (ICANN), Neural Information Processing Systems (NIPS), The IEEE International Symposium on Circuits and Systems (ISCAS), The IEEE Biomedical Circuits and Systems Conference (BioCAS).
  • Has reviewed for several funding bodies, including EPSRC and BBSRC.


Computational Neuroscience, Artificial Intelligence:

  • Synaptic plasticity and Learning
  • Unsupervised Learning
  • Reinforcement Learning

Eleni develops computational models aiming to advance our understanding of the brain learning mechanisms. She targets to unveil the principles that govern the modifications of the neuronal connections when acquiring information, with emphasis to unsupervised and reinforcement learning.


Current grants

  • The cortical representation of low-probability stimuli and its neuromorphic implementation, WELLCOME TRUST (THE), 09/2016 to 09/2019, £246,222, as PI

Previous grants

  • From synaptic plasticity to cortical neuronal networks emergent behaviour, ROYAL SOCIETY, 06/2010 to 05/2012, £8,450, as PI
  • NAMASEN: Neuroelectronics and nanotechnology - towards a Multidisciplinary Approach for the Science and Engineering of Neuronal Networks, EUROPEAN COMMISSION - FP6/FP7, 10/2011 to 09/2015, £215,509, as PI
  • Green Brain: Computational Modelling of the Honeybee Brain, EPSRC, 03/2013 to 08/2016, £660,561, as Co-PI
  • Memristive Dynamics, EPSRC, 05/2012 to 02/2013, £17,271, as PI