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AgWearCare
About Project
Acronym
AgWearCare
Responsible
Duarte Filipe Dias
Status
Closed
Start
January 1, 2021
End
January 30, 2023
Effective End
January 30, 2023
Global Budget
€354,989.00
Financing
€56,035.84
Website
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Members
Team Leaders
Duarte Filipe Dias
João Vinagre
I'm a researcher at LIAAD, the Laboratory of Artificial Intelligence and Decision Support at INESC TEC. I received my PhD from the Faculty of Sciences of the University of Porto in 2016. My research interests are recommender systems, user modeling, personalization, web intelligence and information retrieval. I'm also interested in more fundamental areas of artificial intelligence, such as data stream mining, neural networks and representation learning.
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.
