Professor Eleni Vasilaki
DPhil, CEng, MIET
School of Computer Science
Chair of Bioinspired Machine Learning
Head of the Machine Learning research group
Member of the Complex Systems Modelling research group
e.vasilaki@sheffield.ac.uk
Regent Court (DCS)
Full contact details
Professor Eleni Vasilaki
School of Computer Science
Regent Court (DCS)
211 Portobello
ºù«Ӱҵ
S1 4DP
School of Computer Science
Regent Court (DCS)
211 Portobello
ºù«Ӱҵ
S1 4DP
- Profile
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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 ºù«Ӱҵ as Lecturer, where she is Professor since 2016.
- Research interests
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Bioinspired Machine Learning, Neuromorphic Computing, Computational Neuroscience
Eleni and her team take inspiration from biological principles to design novel, machine learning techniques, and in particular reinforcement learning and reservoir computing methods. They also work closely with material scientists and engineers to design hardware that computes in a brain-like manner.
- Publications
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Journal articles
- Optimising network interactions through device agnostic models.. CoRR, abs/2401.07387.
- Editorial: Focus issue on energy-efficient neuromorphic devices, systems and algorithms.. Neuromorph. Comput. Eng., 3, 40201-40201.
- . Neuromorphic Computing and Engineering, 3(4), 040201-040201.
- Machine learning using magnetic stochastic synapses.. Neuromorph. Comput. Eng., 3, 21001-21001.
- SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations.. IEEE Trans. Neural Networks Learn. Syst., 34, 824-838.
- . Applied Physics Letters, 122(4), 040501-040501.
- . Bioinspir Biomim.
- . Scientific Reports, 12.
- . Frontiers in Electronics, 3.
- . IEEE Robotics and Automation Letters, 1-1.
- . Proceedings of the National Academy of Sciences, 118(49).
- Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics.
- . Scientific Reports, 11(1).
- . Scientific Reports, 11(1).
- . Machine Learning, 110(8), 1975-2003.
- . Applied Physics Letters, 118(20).
- . Scientific Reports, 11.
- . Proceedings of the Royal Society B: Biological Sciences, 288(1945).
- . Pattern Recognition Letters, 142, 65-71.
- . Nature Machine Intelligence, 2(3), 155-156.
- Memristors - From In-Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio-Inspired Computing.. Adv. Intell. Syst., 2, 2000085-2000085.
- . Stress, 23(1), 37-49.
- . DMM Disease Models and Mechanisms, 12(9).
- . Scientific Reports, 8(1), 15089-15089.
- . PLoS Computational Biology, 14(9).
- . PLoS ONE, 12(2).
- . PLoS Computational Biology, 12(5).
- . Scientific Reports, 6.
- . Scientific Reports, 5.
- . Frontiers in Computational Neuroscience, 8.
- . PLoS ONE, 9(7).
- . Frontiers in Neuroinformatics, 8, 26-26.
- . Frontiers in Computational Neuroscience, 7, 192-192.
- . PLoS ONE, 9(1).
- . PLoS One, 6(5), e18539.
- . Nat Neurosci, 13(3), 344-352.
- A biologically inspired dynamic model for vision..
- . PLoS Comput Biol, 5(12), e1000586.
- . PLoS Comput Biol, 5(12), e1000617.
- . BMC Neuroscience, 10(Suppl 1), P192-P192.
- . Biol Cybern, 100(4), 319-330.
- . Biol Cybern, 100(2), 147-158.
- . PLoS Comput Biol, 4(12), e1000248.
- . PLoS Comput Biol, 3(8), e165.
- Some optimal stochastic control problems in neuroscience - A review. MOD PHYS LETT B, 18(21-22), 1067-1085.
- Towards a Spatio-Temporal Album. WSEAS Trans. on Systems, 2(4), 941-947.
- . IEEE Trans Neural Netw, 14(2), 439-443.
- . Nature Communications, 15(1).
- . Communications Physics, 6(1).
- . PLOS Computational Biology, 19(5), e1009616-e1009616.
- . Nanotechnology.
- . PLOS Computational Biology, 18(8), e1009393-e1009393.
- . Advanced Functional Materials, 2008389-2008389.
- A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning.
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- . Frontiers in Behavioral Neuroscience, 9.
- . PLoS Computational Biology, 5(12).
- . Nature Precedings.
- . Advanced Intelligent Systems, 2000085-2000085.
- SpaRCe: Sparse reservoir computing.
- Exploiting Multiple Timescales in Hierarchical Echo State Networks. Machine Learning.
- EchoVPR: Echo State Networks for Visual Place Recognition.
Conference proceedings papers
- . 2023 IEEE Nanotechnology Materials and Devices Conference (NMDC), 22 October 2023 - 25 October 2023.
- . 2023 IEEE International Flexible Electronics Technology Conference (IFETC), 13 August 2023 - 16 August 2023.
- . Spintronics XIV, 1 August 2021 - 5 August 2021.
- . Biomimetic and Biohybrid Systems : 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings (pp 390-401). Freiburg, Germany, 28 July 2020 - 30 July 2020.
- . Biomimetic and Biohybrid Systems (pp 277-286). Nara, Japan, 9 July 2019 - 12 July 2019.
- . 2017 International Joint Conference on Neural Networks (IJCNN) (pp 4171-4178), 15 May 2017 - 19 May 2017.
- . AIAA Infotech @ Aerospace
- . 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 25 August 2015 - 29 August 2015.
- . BMC Neuroscience, Vol. 16(S1)
- . 2014 International Conference on Electrical Engineering and Information & Communication Technology, 10 April 2014 - 12 April 2014.
- A Web-Based Framework for Semi-Online Parallel Processing of Extracellular Neuronal Signals Recorded by Microelectrode Arrays. MEA Meeting 2014, 9th International Meeting on Substrate-Integrated Microelectrode Arrays, 1 July 2014 - 4 July 2014.
- Memristors as synapse emulators in the context of event-based computation. IEEE International Symposium on Circuits and Systems (ISCAS)
- QSpikeTools: An Open Source Toolbox for Parallel Batch Processing of Extracellular Neuronal Signals Recorded. 2014 1ST INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION & COMMUNICATION TECHNOLOGY (ICEEICT 2014)
- . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8064 LNAI (pp 362-363)
- . JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, Vol. 83 (pp A37-A38)
- . ICANN (1), Vol. 7552 (pp 185-192)
- . ICANN (1), Vol. 7552 (pp 193-200)
- How degrading networks can increase select cognitive functions. Computational Neuroscience and Neurotechnology Bernstein Conference 2011. Freiburg, Germany, 4 October 2011.
- Why is connectivity in barrel cortex different from that in visual cortex? - A plasticity model. COSYNE Computational and Systems Neuroscience. Salt Lake City, Utah, USA., 25 February 2010.
- Pattern sampling-based reinforcement learning via homeostatic plasticity and Hebbian tagging. Society for Neuroscience. San Diego, USA.
- Hebbian reinforcement learning with stochastic binary synapses follows the reward gradient. The Fifteenth Annual Computational Neuroscience Meeting, Edinbourgh, UK.
- Temporal Processing with Volatile Memristors. IEEE International Symposium on Circuits and Systems (ISCAS) 2013. Beijing, China, 19 May 2013 - 23 May 2013.
- Do synaptic dynamics and STDP govern connectivity motifs?. Computational Neuroscience and Neurotechnology Bernstein Conference 2011. Freiburg, Germany, 4 October 2011.
- Spike-based reinforcement in continuous state and action space. Multidisciplinary Symposium on Reinforcement Learning. Montreal, Quebec, Canada., 18 June 2009.
- Spike-based reinforcement learning of navigation. The Seventeenth Annual Computational Neuroscience Meeting. Portland, Oregon, USA, 19 July 2008.
- Spike-based reinforcement learning of navigation. The Seventeenth Annual Computational Neuroscience Meeting. Portland, Oregon, USA, 19 July 2008.
- A unified voltage-based model for STDP, LTP and LTD. COSYNE Computational and Systems Neuroscience. Salt Lake City, Utah, USA., 22 February 2007.
- Learning and forgetting visuo-motor associations with a multi-layer neural network. Brain Inspired Cognitive systems. Molyvos, Island of Lesvos, Greece, 12 October 2006.
- Perceptual learning by modifying top-down connections to V1. The Fifteenth Annual Computational Neuroscience Meeting. Edinbourgh, UK
- A Hebbian reinforcement learning algorithm reproducing monkey performances in visuo-motor learning task. COSYNE Computational and Systems Neuroscience. Salt Lake City, Utah.
- Processing Static Visual Information with IF-networks. Proceedings of the 12th Danish Conference on Pattern Recognition and Image Analysis, 1 August 2003.
- Comparison of Feedforward (TDRBF) and Generative (TDRGBN) Network for Gesture Based Control. Lecture Notes in Artificial Intelligence, Vol 2298
- Analysis Of Phosphorus Diffusion In the Polysilicon/Oxide/Silicon System Under Oxidizing Conditions For Profile Simulation. 194th Meeting, Electrochemical Society. Boston, MA.
- Is Epicurus the father of Reinforcement Learning?. ºù«Ӱҵ Machine Learning Retreat 2017
Software / Code
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Preprints
- , Springer Science and Business Media LLC.
- Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs, arXiv.
- , arXiv.
- Machine learning using magnetic stochastic synapses.
- , arXiv.
- , Research Square Platform LLC.
- Adaptive Programmable Networks for In Materia Neuromorphic Computing.
- , Research Square Platform LLC.
- Reservoir Computing with Emergent Dynamics in a Magnetic Metamaterial.
- , Cold Spring Harbor Laboratory.
- , arXiv.
- , arXiv.
- , arXiv.
- , arXiv.
- , arXiv.
- , arXiv.
- , Cold Spring Harbor Laboratory.
- , Cold Spring Harbor Laboratory.
- , arXiv.
- , arXiv.
- , arXiv.
- , arXiv.
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- Learning sparsity in reservoir computing through a novel bio-inspired
algorithm.
- Detection of multiple and overlapping bidirectional communities within
large, directed and weighted networks of neurons.
- Emulating long-term synaptic dynamics with memristive devices.
- Optimising network interactions through device agnostic models.. CoRR, abs/2401.07387.
- Grants
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Current grants
- MARCH: , EPSRC, 02/2021 - 07/2025, £936,815, as Co-PI
- ActiveAI - , EPSRC, 11/2019 - 10/2024, £953,584, as Co-PI
- Modeling probabilistic reinforcement learning and variable behaviour in the fruit fly Drosophila melanogaster, Google, 05/2018 - 12/2025, £50,769, as PI
Previous grants
- CausalXRL: , EPSRC, 02/2021 - 07/2024, £309,915, as PI
- From Stochasticity to Functionality: , EPSRC, 04/2019 - 11/2023, £755,424, as Co-PI
- Alexa Fellowship, Amazon, 08/2018 - 08/2021, £73,000, as Co-PI
- , EPSRC, 12/2016 - 12/2021, £2,128,934, as Co-P
- From synaptic plasticity to cortical neuronal networks emergent behaviour, Royal Society, 06/2010 - 05/2012, £8,450, as PI
- The cortical representation of low-probability stimuli and its neuromorphic implementation, The Wellcome Trust, 09/2016 - 04/2021, £246,222, as PI
- , EC FP7, 10/2011 - 09/2015, £215,509, as PI
- , EPSRC, 03/2013 - 08/2016, £660,561, as Co-PI
- Memristive Dynamics, EPSRC, 05/2012 - 02/2013, £17,271, as PI
- Professional activities and memberships
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Inge Strauch Visiting Professor by the University of Zurich (1 September 2021-28 February 2022).
One of the Academic editors of Scientific Reports, Nature Publishing Group, PLOS ONE and PeerJ.
Advisory Board Member for the research Centre For Emergent Algorithmic Intelligence at the University of Mainz, Germany, since 2019.
More than 50 invited talks, including international leading Institutions, e.g. IIT Roorkee, India (2020), Nature Conference for Neuromorphic Computing, Beijing, China (2019), European Institute for Theoretical Neuroscience (EITN), Paris (2018), Beijing Institute of Technology, China (2017), University of Oxford, UK (2016), Imperial College, UK (2015), University La Sapienza of Rome, Italy (2014), ETH Zurich & University of Zurich, Switzerland (2013), Columbia University, USA (2013) and keynote talks (e.g. International Multi-Conference on Artificial Intelligence Technology (M-CAIT2021).