Eddy Maddalena
Hi, I am Eddy Maddalena,
this is my personal page. I am a tenure track researcher in the Dept. of Mathematics, Computer Science and Physics at the University of Udine, Italy. On this page, you'll find some information about me, including my works, research interests, publications list, and contacts.

Bio

I graduated (Bachelor's degree) in Multimedia and Web Technologies in University of Udine, then, in the same institute I graduated (Master’s Degree) in Multimedia Communication and Information Technologies, with mark 110/110 cum laude.

In 2017 I completed my PhD in Computer Science at the University of Udine under the supervision of Prof. Stefano Mizzaro. I defended my thesis entitled Crowdsourcing Relevance: Two Studies on Assessment. During my Ph.D. studies, I had the opportunity to spend six months, from July to December 2014, as visiting student at the Royal Melbourne Institute of Technology (RMIT), Australia. Meantime, I did an internship in SEEK Ltd, which runs Australia's number one website for job seekers. From April to July 2016 I got the pleasure to be hosted for four months at the Information School of University of Sheffield (UK).

From July 2017 to January 2020 I worked as research fellow in the Web and Internet Science (WAIS) group at the University of Southampton (UK).

In February 2020, I joined the Distributed Artificial Intelligence (DAI) group at the King's College London (UK) as research fellow.

In February 2022, I returned to the University of Udine for a tenure truck position.

Research projects

Logo of the H2020 Qrowd project
Qrowd

European cities face daily problems with the mobility of their inhabitants and visitors, as well as with the delivery of goods and services along their streets and connecting roads. QROWD is a H2020 project that offers local government and transportation businesses innovative solutions to reduce congestion, make mobility safer and more efficient, and improve the use of urban infrastructures and reduced travel times – ultimately enhancing quality of life in European cities. To achieve this, QROWD will integrate different sources of data – maximizing the value of Big Data in planning and managing urban traffic and mobility. We aim to integrate geographic, transport, meteorological, cross-domain and news data, in order to capitalize on hybrid Big Data integration and analytics methods, while efficiently combining algorithms and human computation incorporated in the entire Big Data Value Chain.

From July 2017 to November 2019 when Qrowd ended I covered the role of leader of the crowdsourcing work-package.

Website: http://qrowd-project.eu/ Grant agreement ID: 732194

Logo of the H2020 Cleopatra progect
Cleopatra

Cleopatra is a EU 2020 Marie Skłodowska-Curie ITN (Innovative Training Network) project that aims to make sense of the massive digital coverage generated by the intense disruption in Europe over the past decade – including appalling terrorist incidents and the dramatic movement of refugees and economic migrants. Cleopatra offers a unique interdisciplinary and cross-sectoral research and training programme, which will explore how we can begin to analyse and understand the major events that influence and shape our lives and our societies. It will facilitate advanced cross-lingual processing of textual and visual information related to key contemporary events at scale, and will develop innovative methods for efficient and intuitive user access to and interaction with multilingual information.

Since the beginning of Cleopatra in January 2019, I am involved in the project covering the role of mentor to early-stage researchers. Such a position requires to provide to fellows with guidance and support. This commitment includes the organisation of international events such as the Research and Development Week and Hackaton held in March and April 2020.

Website: http://cleopatra-project.eu/ Grant agreement ID: 812997

Logo of the H2020 Action project
Action

The ACTION (Participatory science toolkit against pollution) project is co-funded by the European Commission under the Horizon 2020 framework, SwafS programme and started on 1st February 2019. Action will transform the way we do citizen science (CS) today: from a mostly scientist-led process to a more participatory, inclusive, citizen-led one, which acknowledges the diversity of the CS landscape and of the challenges CS teams have to meet as their project evolves. We have partnered with 5 European CS initiatives tackling major forms of pollution, which pose substantial threats to human health and to the environment, and contributing to Sustainable Development Goals. These pilots will be the starting point for a ‘citizen science accelerator’, which will be expanded through an open call.

My role in actions is to study the factors that motivate volunteer participation in taking part in citizen science initiatives.

Website: https://actionproject.eu/ Grant agreement ID: 824603

Logo of the H2020 They buy for you project
They Buy For You

The Buy For You (TBFY) is an European Union's Horizon 2020 research and innovation programme that ueses transparency data on government procurement, enriched and integrated with other open PSI sources, and processed and analysed using cutting-edge data science methods, sheds light on the opportunities and challenges faced by all direct stakeholders in taking more informed and effective decisions. By delivering an open-source platform and APIs which can be used to implement a wide range of procurement business cases, we help procurement service providers and IT companies operating in this space innovate – they can build upon TheyBuyForYou technology and our procurement knowledge graph to deliver bespoke commercial products and services.

My main contribution in the TBFY project concerns the development of techniques that combine artificial intelligence and crowdsourcing to automatically extract and elaborate information from tender documents.

Website: https://theybuyforyou.eu/ Grant agreement ID: 780247

Publications

Teaching

Currently, I hold the following courses: Past experiences: