
About Project
AI for REAL-world NETwork operation
Acronym
AI4REALNET
Responsible
Ricardo Jorge Gomes de Sousa Bento Bessa
Status
active
Start
January 1, 2023
End
January 31, 2027
Effective End
--
Global Budget
€3,999,976.25
Financing
€516,975.00
Members
Team Leaders

Ricardo Bessa was born in 1983 in Viseu, and received his Licenciado (5-years) degree from the Faculty of Engineering of the University of Porto, Portugal (FEUP) in 2006 in Electrical and Computer Engineering. In 2008, he received the M.Sc. degree in Data Analysis and Decision Support Systems from the Faculty of Economics of the University of Porto (FEP). He obtained his Ph.D. degree in the Doctoral Program in Sustainable Energy Systems (MIT Portugal) at FEUP in 2013. Currently, he is Coordinator of the Center for Power and Energy Systems at INESC TEC. He worked on several international projects such as the European Projects FP6 ANEMOS.plus, FP7 SuSTAINABLE, FP7 evolvDSO, Horizon 2020 UPGRID, Horizon 2020 InteGrid, H2020 Smart4RES, H2020 InterConnect, HORIZON ENERSHARE, and an international collaboration with Argonne National Laboratory for the U.S. Department of Energy. At the national level, he participated in the development of renewable energy forecasting systems and consultant services about energy storage and AI. Associate Editor of IEEE Transactions on Sustainable Energy and received the ESIG Excellence Award in 2022. He is co-authors of more than 60 journal papers and 120 conference papers, and IEEE Senior Member.


Sara Neves works as a researcher at INESC TEC, focusing on Innovation, Technology, and Entrepreneurship, and is an Invited Assistant Professor at the School of Economics and Management, University of Porto. Her research primarily focuses on university entrepreneurship, knowledge valorisation, and digital transformation. She completed her PhD in Management at the University of Porto (2022), examining knowledge-based value creation in university entrepreneurship. She has extensive teaching experience across undergraduate and graduate programs, including Management of Technology, Marketing, Economics, and Statistics courses.

Team Members

Catarina Esmeralda Ramos de Carvalho

Pedro Gabriel Dias Ferreira

Duarte Filipe Dias

Susana Cristina Marques Pais Rodrigues

José Pedro Ferreira Pelicano Paulos

Sara Lúcia Correia Neves

João Paulo Trigueiros da Silva Cunha

Ricardo Jorge Gomes de Sousa Bento Bessa

Maria Alexandra Neves Soares Reis Torgal Lobo Xavier
Associated Centres
Biomedical Engineering Research
The impact that science and innovation can have on the prevention, early detection, and support for the diagnosis of various types of diseases is fully explored at our Centre for Biomedical Engineering Research (C-BER). Guided by an interdisciplinary approach that prioritises technology transfer with economic impact—through the creation of new systems, tools, and methods related to disease diagnosis and monitoring, ageing, human rehabilitation, physiotherapy, and functional assessment—our researchers are dedicated to developing advanced technologies positioned at the intersection of engineering, medicine and health, and general well-being. Promoting strategic partnerships with clinical partners, research institutes, and encouraging international cooperation is one of the centre’s key priorities. Its research is structured across three distinct areas: Biomedical Imaging, Bioinstrumentation, and Neuroengineering.

Artificial Intelligence and Decision Support
Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.
