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Presentation

Artificial Intelligence and Decision Support

At LIAAD, we work on the very strategic area of Data Science, which has an increasing interest worldwide and is critical to all areas of human activity. The huge amounts of collected data (Big Data) and the ubiquity of devices with sensors and/or processing power offer opportunities and challenges to scientists and engineers. Moreover, the demand for complex models for objective decision support is spreading in business, health, science, e-government and e-learning, which encourages us to invest in different approaches to modelling.

Our overall strategy is to take advantage of the data flood and diversification, and to invest in research lines that will help reduce the gap between collected and useful data, while offering diverse modelling solutions.

At LIAAD, our fundamental scientific principals are machine learning, statistics, optimisation and mathematics.

Latest News
Computer Science

Apps of YAKE! and Conta-me Histórias are already available on Google Play

The scientific research projects YAKE! and Conta-me Histórias are already available in app format on Google Play.

14th October 2019

Computer Science

INESC TEC organises international workshop on recommendation systems and user modelling

A team of INESC TEC’s researchers was responsible for the organisation of the international workshop ORSUM 2019 – 2nd Workshop on Online Recommender Systems and User Modelling, focused on incremental models for user recommendation and modelling. 

07th October 2019

Computer Science

INESC TEC's researcher was honoured by the Artificial Intelligence Society

On 12 September, Pavel Brazdil, researcher and founder of INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD) was honoured by the Portuguese Association for Artificial Intelligence (APPIA) in Guimarães, in the closing session of the Gulbenkian Workshop “New Talents in Artificial Intelligence”. 

25th September 2019

Computer Science

INESC TEC researchers edit publication on narrative processing

Alípio Jorge, Ricardo Campos and Sérgio Nunes, alongside Adam Jatowt were the invited editors of Special Issue on Narrative Extraction from Texts.

09th July 2019

Computer Science

INESC TEC team wins Best Demo Presentation Award

A team from INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD), composed of the coordinator, Alípio Jorge, and the researchers Arian Pasquali, Vítor Mangaravite and Ricardo Campos, alongside Adam Jatowt, won the Best Demo Presentation award at ECIR 2019 (41st European Conference on Information Retrieval) with the project “Conta-me Histórias” (in English "Tell me Sotries").

14th May 2019

Interest Topics
049

Featured Projects

T4CDTKC

Training 4 Cotec, Digital Transformation Knowledge Challenge - Elaboração de Programa de Formação “CONHECER E COMPREENDER O DESAFIO DAS TECNOLOGIAS DE TRANSFORMAÇÃO DIGITAL”

2019-2019

RISKSENS

Market Risk Sensitivities

2019-2020

NDTECH

NDtech 4.0 - Smart and Connected - Estudo e Caderno de Encargos

2019-2019

PELICAN

Risk Assessment for Microfinance

2019-2020

HOUSEVALUE

Estimativa de Valor de Avaliação de Imóveis

2019-2019

Humane_AI

Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us

2019-2020

MLABA

Machine Learn Based Adaptive Business Assurance

2019-2020

Moveo

Prestação de serviços de investigação e desenvolvimento relativos ao sistema MOVEO

2019-2019

FIN-TECH

A FINancial supervision and TECHnology compliance training programme

2019-2020

FailStopper

Deteção precoce de avarias de veículos de transporte público em ambiente operacional

2018-2020

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2021

MDG

Modelling, dynamics and games

2018-2021

NITROLIMIT

Life at the edge: define the boundaries of the nitrogen cycle in the extreme Antarctic environments

2018-2021

RUTE

Randtech Update and Test Environment

2018-2020

FAST-manufacturing

Flexible And sustainable manufacturing

2018-2021

FLOWTEE

Desenvolvimento de um programa que monitorize automaticamente os níveis de bem-estar (ou felicidade) dos funcionários, a partir de dados disponíveis online

2018-2019

PERS_TOMI

Prestação de Serviços para desenvolvimento de um algoritmo de recomendação PERS como serviço PERSaaS , PERSoff, PERStune e PERSboard

2017-2019

MDIGIREC

Context Recommendation in Digital Marketing

2017-2018

NEXT-NET

Next generation Technologies for networked Europe

2017-2019

PERSONA

PERSONALIZAÇÃO E GESTÃO DE INFORMAÇÃO BASEADA EM DADOS CLIENTE

2017-2017

RECAP

Research on European Children and Adults born Preterm

2017-2021

SmartFarming

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

PANACea

Perfis para Anomalias Consumo

2016-2019

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

CORAL-TOOLS

CORAL – Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

MarineEye

MarinEye - A prototype for multitrophic oceanic monitoring

2015-2017

FOUREYES

TEC4Growth - RL FourEyes - Intelligence, Interaction, Immersion and Innovation for media industries

2015-2019

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

NanoStima-RL4

NanoSTIMA - Health Data Analysis & Decision

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

FOTOCATGRAF

Graphene-based semiconductor photocatalysis for a safe and sustainable water supply: an advanced technology for emerging pollutants removal

2015-2018

SEA

SEA-Sistema de ensino autoadaptativo

2015-2015

MAESTRA

Learning from Massive, Incompletely annotated, and Structured Data

2014-2017

BI4UP

Business Intelligence (BI) Tool

2014-2014

SIBILA

Towards Smart Interacting Blocks that Improve Learned Advice

2013-2015

SmartManufacturing

Smart Manufacturing and Logistics

2013-2015

SmartGrids

Smart Grids

2013-2015

Dynamics

Dynamics and Applications

2012-2015

e-Policy

Engineering for the Policy-making Life Cycle (ePolicy)

2011-2014

SIMULESP

Expert system to support network operator on real time decision

2011-2015

CRN

Trust-aware Automatic E-Contract Negotiation in Agent-based Adaptive Normative Environments

2010-2013

KDUS

Knowledge Discovery from Ubiquitous Data Streams

2010-2013

Palco3.0

Intelligent Web system to support the management of a social network on music

2008-2011

Argos

Wind power forecasting system

2008-2012

MOREWAQ

Monitoring and Forecasting of Water Quality Parameters

2008-2011

ORANKI

Resource-bounded outlier detection

2008-2011

Team
Publications

LIAAD Publications

View all Publications

2019

Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, PACBB 2018, Toledo, Spain, 20-22 May, 2018.

Abstract

2019

Impact of genealogical features in transthyretin familial amyloid polyneuropathy age of onset prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Advances in Intelligent Systems and Computing

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient’s attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features. © Springer Nature Switzerland AG 2019.

2019

The 2nd International Workshop on Narrative Extraction from Text: Text2Story 2019

Authors
Jorge, AM; Campos, R; Jatowt, A; Bhatia, S;

Publication
Lecture Notes in Computer Science - Advances in Information Retrieval

Abstract

2019

Guest Editorial: Special Issue on Data Mining for Geosciences

Authors
Jorge, A; Lopes, RL; Larrazabal, G; Nikhalat Jahromi, H;

Publication
Data Mining and Knowledge Discovery

Abstract

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publication
Journal of Medical Systems

Abstract

Supervised Theses

2018

Hybrid coding taxonomy for clinical data harmonization in safe havense

Author
Michael André Pinto Domingues

Institution
UP-FEUP

2018

Natural Language Inference using Relational Commonsense Knowledge

Author
Daniel Alexandre Bouçanova Loureiro

Institution
UP-FCUP

2018

Mobility Patterns from Data

Author
Thiago de Andrade Silva

Institution
UP-FCUP

2018

R&D investments and Dynamics on costs in Cournot competition

Author
Atefeh Afsar

Institution
UP-FCUP

2018

Tensor-based Approaches for Evolving Social Network Analysis

Author
Sofia da Silva Fernandes

Institution
UP-FCUP

Facts & Figures

41Other Funding Programmes (k€)

2016

29Academic Staff

2016

36Papers in indexed journals

2016