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Sobre

Sobre

João Gama é Professor Associado da Faculdade de Economia da Universidade do Porto. É investigador e vice-diretor do LIAAD, INESC TEC. Concluiu o doutoramento na Universidade do Porto, em 2000. É Sénior member do IEEE. Trabalhou em vários projetos nacionais e europeus sobre sistemas de aprendizagem incremental e adaptativo, descoberta de conhecimento em tempo real, e aprendizagem de dados massivos e estruturados. Foi PC chair no ECML2005, DS2009, ADMA2009, IDA '2011 e ECML / PKDD'2015. Foi track chair ACM SAC de 2007 a 2018. Organizou uma série de Workshops sobre Descoberta de Conhecimento de fluxos de dados no ECMLPKDD, ICML, e no ACM SIGKDD. É autor de vários livros em Data Mining e autoria de uma monografia sobre Descoberta de Conhecimento a partir de fluxos de Dados. É autor de mais de 250 papéis peer-reviewed em áreas relacionadas com a aprendizagem automática, aprendizagem de dados em tempo real e fluxos de dados. É membro do conselho editorial de revistas internacionais ML, DMKD, TKDE, IDA, NGC e KAIS. Supervisionou mais de 15 estudantes de doutoramento e 50 alunos de mestrado.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Gama
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 abril 2009
014
Publicações

2023

Online Anomaly Explanation: A Case Study on Predictive Maintenance

Autores
Ribeiro, RP; Mastelini, SM; Davari, N; Aminian, E; Veloso, B; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.

2023

An Online Data-Driven Predictive Maintenance Approach for Railway Switches

Autores
Tome, ES; Ribeiro, RP; Veloso, B; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
An online data-driven predictive maintenance approach for railway switches using data logs obtained from the interlocking system of the railway infrastructure is proposed in this paper. The proposed approach is detailed described and consists of a two-phase process: anomaly detection and remaining useful life prediction. The approach is applied to and validated in a real case study, the Metro do Porto, from which seven months of data is available. The approach has been revealed to be satisfactory in detecting anomalies. The results open the possibilities for further studies and validation with a more extensive dataset on the remaining useful life prediction.

2023

Ethical and Technological AI Risks Classification: A Human Vs Machine Approach

Autores
Teixeira, S; Veloso, B; Rodrigues, JC; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

2023

Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

Autores
Davari, N; Veloso, B; Ribeiro, RP; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Abstract
The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system's dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.

2023

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I

Autores
Koprinska, I; Mignone, P; Guidotti, R; Jaroszewicz, S; Fröning, H; Gullo, F; Ferreira, PM; Roqueiro, D; Ceddia, G; Nowaczyk, S; Gama, J; Ribeiro, RP; Gavaldà, R; Masciari, E; Ras, ZW; Ritacco, E; Naretto, F; Theissler, A; Biecek, P; Verbeke, W; Schiele, G; Pernkopf, F; Blott, M; Bordino, I; Danesi, IL; Ponti, G; Severini, L; Appice, A; Andresini, G; Medeiros, I; Graça, G; Cooper, LAD; Ghazaleh, N; Richiardi, J; Miranda, DS; Sechidis, K; Canakoglu, A; Pidò, S; Pinoli, P; Bifet, A; Pashami, S;

Publicação
PKDD/ECML Workshops (1)

Abstract

Teses
supervisionadas

2022

Mobility Patterns from Data

Autor
Thiago de Andrade Silva

Instituição
UP-FEUP

2022

Forecasting for Solar Power Farms

Autor
Tiago Mourão Pires

Instituição
UP-FEP

2022

Knowledge Discovery from data of spare parts transactions in the energy industry

Autor
Jovelson Aguilar Sabino Junior

Instituição
UP-FEP

2022

European Central Bank Speeches: A Sentiment Analysis Case Study

Autor
Gustavo de Oliveira Vital

Instituição
UP-FEP

2022

Incremental Temporal Interval Mining Methodologies

Autor
Ana Micaela Gomes Batista

Instituição
UP-FCUP