The challenges of the extreme-scale multi-modal analytics applications

A MARVEL Workshop at the Data Week 2022

MARVEL is organizing “The challenges of the extreme-scale multi-modal analytics applications”, a workshop at the DataWeek 2022.

The aim of this workshop is to explore the latest EU research outcomes related to Extreme Data Analytics applications in a variety of sectors and environments and focuses on the key challenges addressed in each sector related to AI methodologies and algorithms, Data Management and orchestration, federation of data experimentation infrastructures and ecosystems. Ways each project addressed those challenges or ways they are planning to address them are going to be presented in combination with some brainstorming for common areas of interest. The audience will have the opportunity to participate, raise their concerns as well as provide suggestions for tackling those challenges.

Info Pack

The challenges of the extreme-scale multi-modal analytics applications


Wednesday, May 25 2022 at 13:30 CET



Register Now!

For more information please visit the event website here

The Special Session is supported by the EU H2020 project Multimodal Extreme Scale Data Analytics for Smart Cities Environments (MARVEL) under GA No 957337.


ExaMode: Extreme-scale Analytics via Multimodal Ontology Discovery & Enhancement.

Exascale volumes of diverse data from distributed sources are continuously produced. Healthcare data stand out in the size produced (production is expected to be over 2000 exabytes in 2020), heterogeneity (many media, acquisition methods), included knowledge (e.g. diagnosis), and commercial value. The supervised nature of deep learning models requires large labeled, annotated data, which precludes models to extract knowledge and value. ExaMode solves this by allowing easy & fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.


OpenDR: Open Deep Learning toolkit for Robotics

The aim of OpenDR is to develop a modular, open, and non-proprietary toolkit for core robotic functionalities by harnessing deep learning to provide advanced perception and cognition capabilities, meeting in this way the general requirements of robotics applications in the applications areas of healthcare, agri-food, and agile production. The term toolkit in OpenDR refers to a set of deep learning software functions, packages, and utilities used to help roboticists to develop and test a robotic application that incorporates deep learning. OpenDR will provide the means to link the robotics applications to software libraries (deep learning frameworks, e.g., Tensorflow) and to link it with the operating environment (ROS). OpenDR will develop, train, deploy and evaluate deep learning models that improve the technical capabilities of the core technologies beyond the current state of the art. It will enable a greater range of robotics applications that can be demonstrated at TRL 3 and above, thus lowering the technical barriers within the prioritised application areas. OpenDR aims to an easily adopted methodology to adapt the provided tools in order to solve any robotics task without restricting it to any specific application.


VesselAI aims at realising a holistic, beyond the state-of-the-art AI-empowered framework for decision-support models, data analytics and visualisations to build digital twins and maritime applications for a diverse set of cases with high impact, including simulating and predicting vessel behaviour and manoeuvring (including the human factor), ship energy design optimisation, autonomous shipping and fleet intelligence. Designing and developing a high-performance, scalable and sustainable decision-making framework for data-driven digital twins includes and requires innovation diffusion and exploitation of network dynamics, and the adaptability to the stakeholders’ needs. In this direction, VesselAI will bring a diverse set of pilot scenarios to the centre of the process, to test and improve the initial proposition, and provide a truly innovative solution that makes extreme-scale decision analysis, using novel data engineering and machine learning techniques, a native engineering characteristic

Dr. Manfredo Atzori

Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)

Dr. Manfredo Atzori is a research scientist at the Institute of Information Systems of the University of Applied Sciences Western Switzerland (HES-SO Valais) since 2011 and is an Assistant Professor of Deep Learning Applied to Neuroscience and Rehabilitation at the University of Padova (Italy) since 2021. He is the Scientific Coordinator of the Horizon 2020 project ExaMode, involving seven international partners and targeting weakly-supervised knowledge discovery from multimodal medical data, such as text and images, in the context of digital pathology (htttp:// He has an M.Sc. in Physics, a Ph.D. in Information Engineering, and over 15 years of experience in the analysis of biomedical data. His research interests are related to applied machine learning and multimodal data analysis, in particular targeting autonomous learning from multimodal data and the application of machine learning to medicine and assistive robotics. Dr. Atzori is author of over 80 peer-reviewed scientific publications with over 2’800 citations and he presented his work, also as an invited speaker, at several renown international conferences.

Dr. Nikolaos Passalis

Postdoctoral Researcher, Aristotle University of Thessaloniki, Greece

Nikolaos Passalis received the B.Sc. degree in Informatics, the M.Sc. degree in Information Systems, and the Ph.D. degree in Informatics from the Aristotle University of Thessaloniki, Thessaloniki, Greece, in 2013, 2015, and 2018, respectively. Since 2019, he has been a post-doctoral researcher with the Aristotle University of Thessaloniki, while from 2018 to 2019 he also conducted post-doctoral research at the Faculty of Information Sciences, Tampere University, Finland. He has co-authored 50 journal papers, 65 papers in international conferences and contributed 8 chapters to edited books in his area of expertise. Over 1700 citations have been recorded to his publications and his h-index is 23 according to Google Scholar. He is a member of the Technical Management Team of the Open Deep Learning toolkit for Robotics (OpenDR). His research interests include deep learning, information retrieval, time-series analysis, and computational intelligence.

Dr. Paulo Figueiras

Researcher at UNINOVA , Portugal

Paulo Figueiras has a Ph.D. and MSc in Electrotechnical and Computers Engineering and is a researcher at the UNINOVA center. Deeply focused on the areas of knowledge management and the semantic web since 2009 and in the Big Data and Internet of Things domains since he started at UNINOVA in 2012, Paulo has been working on several academic and research projects, namely FP7-ICT-318452 MobiS, FP7-ICT-288315 PROBE-IT and FP7-ICT-612329 PROASENSE, H2020-636160 OPTIMUM or H2020-780732 BOOST 4.0. Main responsibilities within these projects are software development, technical team management, Data Engineering, Data Science, setup and deployment of Big Data solutions, user requirement elicitation, and support testing and validation of proofs-of-concept.

MARVEL DataWeek2022 Special Session Agenda

13:30 - 13:45Introduction MARVEL OverviewProf. Sotiris Ioannidis, Coordinator of project MARVEL
13:45 - 14:00ExaMode OverviewDr. Manfredo Atzori, Scientific Coordinator of project ExaMode
14:00 - 14:15OpenDR OverviewDr. Nikolaos Passalis, Technical Manager of project OpenDR
14:15 - 14:30VesselAIDr. Paulo Figueiras
14:30 - 14:45Round table discussion among project representatives
14:45 - 15:00Q&A with the audience

Workshop Organiser

Prof. Sotiris Ioannidis

MARVEL Project Coordinator, FORTH

Prof. Sotiris Ioannidis received a BSc degree in Mathematics and an MSc degree in Computer Science from the University of Crete in 1994 and 1996 respectively. In 1998 he received an MSc degree in Computer Science from the University of Rochester and in 2005 he received his Ph.D. from the University of Pennsylvania. Ioannidis held a Research Scholar position at the Stevens Institute of Technology until 2007 and a Research Director at the Foundation for Research and Technology – Hellas (FORTH) until 2020. He is currently Associate Professor at the School of Electrical and Computer Engineering of the Technical University of Crete (TUC) and Director of the Microprocessor and Hardware Laboratory. He was a Member of the ENISA Advisory Group (AG) from 2017 to 2020, and is a Member of the National Infrastructures for Research and Technology (GRNET) Advisory Committee (AC).

He is also Chairman of the of the Committee of Ethics and Deontology of Research of FORTH and Member of the Advisory Committee for National Infrastructures for Research and Technology. His research interests are in the area of systems and network security, security policy, privacy, and high-speed networks. Ioannidis has authored more than 200 publications in international conferences and journals, as well as book chapters, and has both chaired and served on numerous program committees in prestigious conferences, such as ACM CCS and IEEE S&P. Ioannidis is a Marie-Curie Fellow and has participated in numerous international and European projects. He has been the PI of 40 European, National and DARPA projects, attracting in excess of 12 million euros for his organization, and has been Project Coordinator in 14 of them.

Workshop Moderators

Kopanaki Despoina

Despina Kopanaki

Project Manager at FORTH

Despina Kopanaki is a project manager at the Institute of Computer Science (ICS) of the Foundation for Research and Technology – Hellas (FORTH). She has participated in the project coordination and project management of I-BiDaaS and currently of the MARVEL (Multimodal Extreme Scale Data Analytics for Smart Cities Environments, GA #957337) EU funded projects. Within MARVEL, she is also leading the ethics compliance activities. Before that, she has been involved in more than 20 European and National projects in the area of mobility, data mining, privacy, big data, and data analytics as a researcher, project manager, and financial manager. She holds a Bachelor Degree from the Department of Statistics and an MSc in Applied Economics & Finance from the Department of Economics, both from Athens University of Economics and Business (AUEB), Greece. She is also a Ph.D. candidate in Informatics at the Department of Informatics, University of Piraeus, Greece. Her research interest is focused on data analytics and privacy-preservation of mobility data.

Theodora Kalipollitou

Delivery Manager at Zelus P.C.

Theodora Kallipolitou (she/her), works as a Delivery Manager in Zelus P.C. since 2020. She holds a BSc in Marketing and Communication from Athens University of Economics and Business (AUEB), Greece. She also holds a MSc in Management of Innovation from Rotterdam School of Management (ERASMUS University) in the Netherlands, with a specialisation in Sustainable Innovation and Business Model Innovation. She has been working as Business Consultant since 2014, in the field of User Experience and Business Design with extensive experience in practising Design Thinking, Systems Thinking and Agile Coaching. She has worked across multiple industries including Consumers’ products, Retail, Financial Services and Banking and for a short period in Mining. Her interests include the exploitation of digital technologies for the development of sustainable products and services that would thrill their users and contribute to the transition from the Linear to the Circular Economy.



Key Facts

  • Project Coordinator: Dr. Sotiris Ioannidis
  • Institution: Foundation for Research and Technology Hellas (FORTH)
  • E-mail: marvel-info{at} 
  • Start: 01.01.2021
  • Duration: 36 months
  • Participating Organisations: 17
  • Number of countries: 12

Get Connected



This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.