Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Presentation

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.

news
Artificial Intelligence

Portugal’s new large language model unveiled: AMALIA bears the INESC TEC signature

AMALIA is the name of Portugal’s newest large language model (LLM), and INESC TEC was one of the partners behind the project, which was officially unveiled on 1 July at the Innovation Centre of Instituto Superior Técnico, University of Lisbon.

07th July 2026

Artificial Intelligence

Alípio Jorge, researcher at INESC TEC, joins new rectoral team at the University of Porto

Alípio Jorge, researcher at INESC TEC, where he coordinates the Artificial Intelligence area, has joined the new rectoral team at the University of Porto, taking on the role of Vice-Rector.

12th June 2026

Artificial Intelligence

INESC TEC brought citizens closer to local politics with AI – now the technology is available to everyone

The Artificial Intelligence models developed within the CitiLink project are now easily accessible, opening the doors to collaboration and the continuous evolution of solutions aimed at increasing municipal transparency. 

16th April 2026

Artificial Intelligence

ECIR 2026: INESC TEC presented information retrieval research at Europe’s leading conference in the field

For five days, Delft University of Technology (TU Delft) became the meeting point for the international scientific community dedicated to information retrieval, bringing together around 500 experts to discuss search and recommendation models, the integration of language models, and the challenges associated with evaluating and improving the efficiency of these systems. INESC TEC took part in the 48th edition of the European Conference on Information Retrieval (ECIR 2026) with a team of researchers presenting several scientific papers and organising a workshop.

13th April 2026

Everything happening in the world of Artificial Intelligence addressed in INESC TEC’s Tertulia AI 

The first Tertulia AI of 2026 placed Responsible AI centre stage; researchers at INESC TEC have built a community in constant interaction and, through this event, showcase different perspectives on the topics under discussion. 

24th February 2026

Interest
Topics
030

Featured Projects

CardioComplete

Automatic Complete Reporting of Cardiovascular Findings in Opportunistic Computed Tomography Screening

2026-2027

ONAIOPS2

Online Artificial Intelligence for IT Operations 2.0

2026-2028

ATLAS

Atlantic Tracking with Lightwave Acoustic Sensing

2025-2028

SMARTBOGIE

SMARTBOGIE – Solução Inovadora para a Mobilidade Ferroviária de Mercadorias

2025-2028

PTX

Personal Trainer eXpanded

2025-2028

FOXPM

Failure Online eXplanation for Predictive Maintenance

2025-2028

ACLART

artificial intelligence-driven in silico simulation for prediction of anterior cruciate ligament reconstruction clinical outcomes

2025-2028

GREENGROCER

Maturing food footprint tools, solutions, and uptake to accelerate a rapid EU food transition

2025-2029

ScopeAI

Sistema inteligente de caracterização e otimização de produto alimentar com base na experiência de consumidores.

2025-2028

ATAI

Aplicação de técnicas avançadas na gestão de escalas

2025-2026

PROD_AI

Solução IA/ML preditiva aplicada ao procurement e gestão de produção:

2025-2028

CitiLink

CitiLink - Enhancing municipal transparency and citizen engagement through AI: from unstructured to structured data

2025-2026

OBSERVA

Optical Signals and Acoustic Surveillance Observatory

2025-2026

EnSafe

Enhancing Environmental Protection: Anomaly Detection in Waste Transportation using Network Science

2025-2026

NEIS

Deteção de novas construções utilizando Inteligência Artificial e imagens de muito grande resolução espacial

2025-2026

TSP2Net

Time Series Privacy-Preserving: New Approaches via Complex Networks

2025-2027

Easy4ALL

AI Assistant for No-Code Plataform

2024-2026

NuClim

Nuclear observations to improve Climate research and GHG emission estimates

2024-2028

AI4REALNET

AI for REAL-world NETwork operation

2023-2027

AIBOOST

Artificial intelligence for better opportunities and scientific progress towards trustworthy and human-centric digital environment

2023-2027

StorySense

Reaching the Semantic Layers of Stories in Text

2023-2026

ATE

Alliance for Energy Transition

2023-2026

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

2022-2026

Produtech_R3

Agenda Mobilizadora da Fileira das Tecnologias de Produção para a Reindustrialização

2022-2026

EMERITUS

Environmental crimes’ intelligence and investigation protocol based on multiple data sources

2022-2025

FAIST

Fábrica Ágil Inteligente Sustentável e Tecnológica

2022-2026

ADANET

Internet das Coisas Assistida por Drones

2022-2026

FORM_I40

Formação Indústria 4.0

2022-2022

THEIA

Automated Perception Driving

2022-2023

HfPT

Health from Portugal

2021-2026

Team
  • a
  • b
  • c
  • d
  • e
  • f
  • g
  • h
  • i
  • j
  • k
  • l
  • m
  • n
  • o
  • p
  • q
  • r
  • s
  • t
  • u
  • v
  • w
  • x
  • y
  • z
Publications

LIAAD Publications

View all Publications

2026

Resilience Under Attack: Benchmarking Optimizers Against Poisoning in Federated Learning for Image Classification Using CNN

Authors
Biadgligne, Y; Baghoussi, Y; Li, K; Jorge, A;

Publication
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2025, PT I

Abstract
Federated Learning (FL) enables decentralized model training while preserving data privacy but remains susceptible to poisoning attacks. Malicious clients can manipulate local data or model updates, threatening FL's reliability, especially in privacy-sensitive domains like healthcare and finance. While client-side optimization algorithms play a crucial role in training local models, their resilience to such attacks is underexplored. This study empirically evaluates the robustness of three widely used optimization algorithms: SGD, Adam, and RMSProp-against label-flipping attacks (LFAs) in image classification tasks using Convolutional Neural Networks (CNNs). Through 900 individual runs in both federated and centralized learning (CL) settings, we analyze their performance under Independent and Identically Distributed (IID) and Non-IID data distributions. Results reveal that SGD is the most resilient, achieving the highest accuracy in 87% of cases, while Adam performs best in 13%. Additionally, centralized models outperform FL on CIFAR-10, whereas FL excels on Fashion-MNIST, highlighting the impact of dataset characteristics on adversarial robustness.

2026

Knowledge-Aware Clinical Narrative Extraction Using Ontologies and Knowledge Graphs

Authors
Leite, M; Rb Silva, R; Guimaraes, N; Stork, L; Jorge, A;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2025, PT I

Abstract
Providing healthcare professionals with quick access to structured standardized information enables comprehensive analysis and improves clinical decision-making. However, an important part of the records in health institutions is in the form of free text. This paper proposes a pipeline that automatically extracts medical information from Electronic Medical Records (EMRs), based on large language models (LLMs) and a domain ontology defined and validated in collaboration with a medical expert. The output is a knowledge graph of clinical narratives that can be used to search through repositories of EMRs or discover new facts. We showcase our approach on a set of Portuguese clinical texts of cases of Acute Myeloid Leukemia (AML) guided by one medical expert. We evaluate the quality of the extraction and of the knowledge graph.

2026

LLM-Based Framework for Synthetic Data Generation in Portuguese Clinical NER

Authors
Henriques, L; Guimaraes, N; Jorge, A;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2025, PT I

Abstract
The ever-increasing volume of data produced in Healthcare demands solutions capable of automatically extracting the relevant elements of their narratives. However, given privacy regulations, bureaucratic procedures, and annotation efforts, the development of said solutions via Natural Language Processing (NLP) systems becomes hindered due to training data scarcity. Such scarcity increases when we consider languages and language varieties with lower resource availability, such as European and Brazilian Portuguese. To address this problem, we propose a Large Language Model (LLM)-based SDG (Synthetic Data Generation) framework to generate and annotate synthetic clinical texts for medical Named-Entity Recognition (NER). The SDG framework consists of a system/user prompt augmented with real examples, powered by GPT-4o. Our results show that, by feeding the framework few real clinical annotated texts, we can generate synthetic data capable of increasing the performance of NER models with respect to their non-augmented counterparts. In addition, the reduction of the BLEU scores in the generated texts indicates a decrease in the risk of privacy disclosure while ensuring greater lexical diversity. These results highlight the potential of synthetic data as a solution to overcome human annotation bottlenecks and privacy concerns, laying the groundwork for future research in clinical NLP across tasks, domains, and low-resource languages.

2026

Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part VIII

Authors
Pfahringer, B; Japkowicz, N; Larrañaga, P; Ribeiro, RP; Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (8)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part X

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Pasquali, A; Moniz, N; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (10)

Abstract