Dr Evangelos Pournaras
- Position: Associate Professor
- Areas of expertise: distributed systems; blockchain; distributed intelligence; computational social systems; Internet of Things; Smart Cities; Smart Grids
- Email: E.Pournaras@leeds.ac.uk
- Website: Personal Website | EPOS Project | Twitter | LinkedIn | Googlescholar
Dr. Evangelos Pournaras is an Associate Professor at Distributed Systems and Services group, School of Computing, University of Leeds, UK. He has more than 5 years experience as senior scientist and postdoctoral researcher at ETH Zurich in Switzerland after having completed his PhD studies in 2013 at Delft University of Technology and VU University Amsterdam in the Netherlands.
Evangelos has also been a visiting researcher at EPFL in Switzerland and has industry experience at IBM T.J. Watson Research Center in the USA. Since 2007, he holds a MSc with distinction in Internet Computing from University of Surrey, UK and since 2006 a BSc on Technology Education and Digital Systems from University of Piraeus, Greece.
Evangelos is currently a research fellow in blockchain industry. He has won the Augmented Democracy Prize, the 1st prize at ETH Policy Challenge as well as 4 paper awards and honors. He has published more than 50 peer-reviewed papers in high impact journals and conferences and he is the founder of the EPOS, DIAS, SFINA and Smart Agora projects featured at decentralized-systems.org. He has raised significant funding and has been actively involved in EU projects such as ASSET, SoBigData and FuturICT 2.0.
He has supervised several PhD and MSc thesis projects, while he designed courses in the area of data science and multi-agent systems that adopt a novel pedagogical and learning approach. Evangelos' research interest focus on distributed and intelligent social computing systems with expertise in the inter-disciplinary application domains of Smart Cities and Smart Grids.
My research focuses on the self-management of decentralized networked systems designed to empower citizens’ participation for a more democratic and sustainable digital society.
Designing decentralized and self-managed techno-socio-economic systems that can continuously self-adapt, self-heal, self-repair, self-organize and ultimately self-regulate the production and consumption of our resources, e.g. energy, food supplies, sharing vehicles, etc., are means for a sustainable development as I show in my research. Supply-demand data sharing systems self-regulated via differential privacy mechanisms and distributed ledger systems, i.e. blockchain, can protect citizens’ privacy, while incentivizing a fair data sharing for a high quality of service. System-wide, citizens’ active participation and autonomy raises communication and computational challenges: Data streams need to propagate rapidly and regularly in a distributed network. Citizens’ collective choices often have combinatorial complexity.
Decentralized optimization and collective learning over crowd-sourced computational resources of citizens is the main challenge of my research. Tackling this challenge can have a tremendous impact on a new generation of artificial intelligence for socio-technical systems equipped with socially responsible self-management capabilities applied on emerging application domains of Smart Grids, Smart Cities and their sharing economies.<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>
Research groups and institutes
- Distributed Systems and Services
- Artificial Intelligence
Current postgraduate researchers
<li><a href="//phd.leeds.ac.uk/project/744-distributed-artificial-intelligence-for-collective-decisions-in-smart-cities">Distributed Artificial Intelligence for Collective Decisions in Smart Cities</a></li>
<li><a href="//phd.leeds.ac.uk/project/1610-optimum-agent-based-modelling-with-impact-to-engineering-practice">Optimum agent-based modelling with impact to engineering practice</a></li>