
About Project
Digital Innovation Hub for Artificial Intelligence and High-Performance Computing
Acronym
ATTRACT_DIH
Responsible
Vasco Bernardo Figueiredo Cabral Teles
Status
active
Start
January 1, 2022
End
January 30, 2025
Effective End
--
Global Budget
€5,999,745.73
Financing
€711,015.00
Members
Team Leaders

Vasco Figueiredo Teles (Porto, Portugal, 1977) is a Ph.D. by the MIT Portugal Program in Engineering Design and Advanced Manufacturing with the theme of technology planning and exploitation. He has a background in Electrical Engineering from the Faculty of Engineering - University of Porto (FEUP).
He currently works in technology strategy and licensing at INESC TEC. He has industrial and scientific experience in advanced manufacturing at national and European levels, where he has developed several strategic projects and has been an invited expert in innovation and public policy initiatives.
Among others, he previously worked with PRODUTECH, the Portuguese cluster for production technologies where e.g. he coordinated the cluster’s strategy and technology roadmapping, and with Sonae, the largest Portuguese retailer, developing innovative projects in operations, e-commerce, and tourism, and where he was a co-founder of the Innovation Department.

I am an Assistant Professor at the Department of Informatics of the University of Minho and a Senior Researcher at HASLab/INESC TEC.
My research interests are in dependable distributed systems, in particular with application to dependable distributed database systems, large scale distributed systems and cloud computing management.


He is a senior researcher at INESC TEC since 1998. He is coordinator of HumanISE - Human-centered computing and Information Science
Current research interests include platforms and methods for collaborative research, privacy-preserving distributed computation, the semantic sensor Web (IoT) and Big Data processing.
From October 1996 to December 1997, he was an associate member of CERN - European Laboratory for High Energy Physics, IT Division/Web Office.
His research is applied in two major areas: Personalized Health Research (PHR) and Earth and Ocean Observation Science (EOOS).
The PHR area currently subdivides in: a) personalized Internet-based treatments; and b) human data storage, privacy-preserving processing and controlled FAIR data sharing. In this area, he participates in several European projects, such as ICT4Depression (FP7), E-COMPARED (FP7), STOP Depression (EEA Grant), iCare4Depression (FCT), RECAP Preterm (H2020), EUCAN-Connect (H2020) and iReceptor Plus (H2020). In these projects, he often undertakes the role of responsible for the system's architecture, platform implementation, or technical coordinator.
In the EOOS area he participates in the implementation of the RAIA Observatory (Interreg projects RAIA, RAIA.co, RAIA TEC, MarRisk and RADAR ON RAIA), SeaBioData(EEA Grant), MELOA (H2020) and C4G which is the Portuguese node of EPOS (H2020 EPOS-SP).
Team Members

Vanda Maria da Silva Câmara Ferreira

Rui Carlos Mendes de Oliveira

Shazia Tabassum

Catarina Leonor Antunes Leones Fernandes

Marta Alexandra Braga Amorim Oliveira Barbas Ferreira

Paulo Diogo Boa Nova Ferreira

Luís Filipe Maia Carneiro

Paula Cristina Pereira Rodrigues

Vasco Bernardo Figueiredo Cabral Teles

Ricardo Jorge Teixeira de Sousa
Associated Centres
High-Assurance Software
At the High-Assurance Software Laboratory (HASLab), we improve practice through theory, creating and implementing software that goes beyond mere functionality: we ensure it is correct, resilient, and secure against failures and attacks. Our team of researchers, scientists, and engineers has proven expertise in software engineering, developing methods and tools to design and integrate robust software; in distributed systems, exploring distribution and replication to ensure scalability and reliability; and in information security, addressing cybersecurity challenges and improving systems with advanced, secure cryptographic protocols, thus minimising vulnerabilities. With a multidisciplinary approach supported by solid theoretical principles, we develop innovative solutions for critical software, secure cloud infrastructures, and privacy-aware big data management, driving scientific advancement, innovation, and high-level consultancy. In addition, we complement our core expertise with work in human-computer interaction, programming languages, computational mathematics, and quantum computing - because we believe the future of trustworthy software is built on knowledge and innovation.

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.
