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About

About

Fábio Coelho (Male, PhD) is currently a senior researcher of HASLab, one of INESC TEC's research units. He holds a PhD in Computer Science, in the context of the MAP-i Doctoral Programme, from the universities of Minho, Aveiro and Porto (Portugal). His research is focused on cloud HTAP databases, cloud computing, distributed systems, P2P/ledger based systems and benchmarking. He has several international publications in top-tier conferences, such as SRDS, DAIS and ICPE. He participated in several national and EU projects such as CoherentPaaS, LeanBigData, CloudDBAppliance and Integrid. Currently he works closely with the Power and Energy Centre of INESC TEC in the provisioning of ICT solutions for coordination and distributed communication.

Interest
Topics
Details

Details

  • Name

    Fábio André Coelho
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2014
008
Publications

2023

Privacy-Preserving Machine Learning in Life Insurance Risk Prediction

Authors
Pereira, K; Vinagre, J; Alonso, AN; Coelho, F; Carvalho, M;

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

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.

2023

Privacy-Preserving Machine Learning in Life Insurance Risk Prediction

Authors
Pereira, K; Vinagre, J; Alonso, AN; Coelho, F; Carvalho, M;

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

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.

2023

Analysis of Flexibility-centric Energy and Cross-sector Business Models

Authors
Rodrigues, L; Faria, D; Coelho, F; Mello, J; Saraiva, JT; Villar, J; Bessa, RJ;

Publication
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

2023

Towards MRAM Byte-Addressable Persistent Memory in Edge Database Systems

Authors
Ferreira, LM; Coelho, F; Pereira, JO;

Publication
Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28 - September 1, 2023.

Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2023

Loom: A Closed-Box Disaggregated Database System

Authors
Coelho, F; Alonso, A; Ferreira, L; Pereira, J; Oliveira, R;

Publication
12th Latin-American Symposium on Dependable and Secure Computing

Abstract

Supervised
thesis

2022

Gestão de permissões e acesso a dados para Hyperledger Fabric

Author
João Pedro Araújo Parente

Institution
UM

2022

Towards Tunable Distributed Data Management for IoT

Author
Luís Manuel Meruje Ferreira

Institution
UM

2022

Autonomous Optimization for a Transactional Middleware

Author
Susana Vitória Sá Silva Marques

Institution
UM

2021

Towards Tunable Distributed Data Management for IoT

Author
Luís Manuel Meruje Ferreira

Institution
UM

2021

Data Market for Energy Industry Forecasting

Author
Filipe Daniel Vieira da Silva

Institution
UM