???global.info.a_carregar???
As Leader of Machine Learning Engineer, I have shared transversal responsibilities with a team of around 15 Data Scientists, Data Engineers, and other talented software engineers, open to innovative and technological proposals. I'm involved in many exciting tasks, many of which are related to digital transformation, industry 4.0, and cutting-edge technologies. My work focus is to do applied research and development, to push the state-of-the-art, and make our next-generation products smarter.
Identificação

Identificação pessoal

Nome completo
Andre Pilastri

Nomes de citação

  • Pilastri, Andre

Identificadores de autor

Ciência ID
3617-D209-5BF3
ORCID iD
0000-0002-4380-3220

Endereços de correio eletrónico

  • andre.pilastri@ccg.pt (Profissional)

Websites

Domínios de atuação

  • Ciências Exatas - Ciências da Computação e da Informação - Ciências da Computação
Formação
Grau Classificação
2017
Em curso
 Engenharia Informática (Doutoramento)
Especialização em Sem especialidade
Universidade do Porto Faculdade de Engenharia, Portugal
"Redes Complexas em Visão Computacional - Aplicação na Análise de Imagens Dermatoscópicas" (TESE/DISSERTAÇÃO)
Produções

Publicações

Artigo em conferência
  1. Pilastri, Andre. "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost". 2021.
    10.1109/ijcnn52387.2021.9534091
  2. Matos, Luís Miguel; Domingues, André; Moreira, Guilherme; Cortez, Paulo; Pilastri, André Luiz. "A comparison of machine learning approaches for predicting in-car display production quality". 2021.
    10.1007/978-3-030-91608-4_1
  3. Ribeiro, Diogo Aires Gonçalves; Matos, Luís Miguel; Cortez, Paulo; Moreira, Guilherme; Pilastri, André Luiz. "A comparison of anomaly detection methods for industrial screw tightening". 2021.
    10.1007/978-3-030-86960-1_34
  4. Coelho, Gabriel; Pereira, Pedro; Matos, Luis; Ribeiro, Alexandrine; Nunes, Eduardo C.; Ferreira, André; Cortez, Paulo; Pilastri, André. "Deep dense and convolutional autoencoders for machine acoustic anomaly detection". 2021.
    10.1007/978-3-030-79150-6_27
  5. Pereira, Pedro José; Pereira, Adriana; Cortez, Paulo; Pilastri, André Luiz. "A comparison of machine learning methods for extremely unbalanced industrial quality data". 2021.
    10.1007/978-3-030-86230-5_44
  6. Ferreira, Luís; Pilastri, André Luiz; Martins, Carlos Manuel; Pires, Pedro Miguel; Cortez, Paulo. "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost". 2021.
    10.1109/IJCNN52387.2021.9534091
  7. Pereira, Pedro José; Coelho, Gabriel José Dias; Ribeiro, Alexandrine; Matos, Luís Miguel Rocha; Nunes, Eduardo Carvalho; Ferreira, André; Pilastri, André Luiz; Cortez, Paulo. "Using deep autoencoders for in-vehicle audio anomaly detection". 2021.
  8. Pilastri, Andre. "Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology". 2020.
    10.5220/0009411205480555
  9. Pilastri, Andre. "An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management". 2020.
    10.5220/0008952800990107
  10. Ribeiro, Rui; Pilastri, André; Moura, Carla; Rodrigues, Filipe; Rocha, Rita; Morgado, José; Cortez, Paulo. "Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features". 2020.
    10.1007/978-3-030-49186-4_21
  11. Ferreira, Luís; Pilastri, André; Martins, Carlos; Santos, Pedro; Cortez, Paulo. "An automated and distributed machine learning framework for telecommunications risk management". 2020.
    10.5220/0008952800990107
  12. Silva, António João; Cortez, Paulo; Pilastri, André. "Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples". 2020.
    10.1007/978-3-030-49186-4_20
  13. Ribeiro, Rui; Pilastri, André; Moura, Carla; Rodrigues, Filipe; Rocha, Rita; Cortez, Paulo. "Predicting the tear strength of woven fabrics via automated machine learning: an application of the CRISP-DM methodology". 2020.
    10.5220/0009411205480555
  14. Andre Pilastri; João Papa; João Manuel R. S. Tavares. "Segmentation of Skin in dermatoscopic images using SuperPixels combined with Complex Networks". 2018.
  15. Ferrarezi, J.C.; Neto, M.P.; Dias, D.R.C.; Pilastri, A.L.; De Paiva Guimarães, M.; Brega, J.R.F.. "LibViews - An information visualization application for third-party libraries on software projects". 2016.
    10.1109/IV.2016.43
  16. Fernandes, S.E.N.; Pilastri, A.L.; Pereira, L.A.M.; Pires, R.G.; Papa, J.P.. "Learning kernels for support vector machines with polynomial powers of sigmoid". 2014.
    10.1109/SIBGRAPI.2014.36
Artigo em revista
  1. António João Silva; Paulo Cortez; Carlos Pereira; André Pilastri. "Business analytics in Industry 4.0: A systematic review". Expert Systems (2021): https://doi.org/10.1111/exsy.12741.
    10.1111/exsy.12741
  2. Silva, António João; Cortez, Paulo; Pereira, Carlos; Pilastri, André. "Business analytics in industry 4.0: a systematic review". (2021): http://hdl.handle.net/1822/73739.
    10.1111/exsy.12741
  3. Pilastri, Andre. "Using Deep Autoencoders for In-vehicle Audio Anomaly Detection". Procedia Computer Science 192 (2021): 298-307. http://dx.doi.org/10.1016/j.procs.2021.08.031.
    10.1016/j.procs.2021.08.031
Capítulo de livro
  1. Pilastri, Andre. "A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data". 561-572. Springer International Publishing, 2021.
    10.1007/978-3-030-86230-5_44
  2. Gabriel Coelho; Pedro Pereira; Luis Matos; Alexandrine Ribeiro; Eduardo C. Nunes; André Ferreira; Paulo Cortez; André Pilastri. "Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection". 337-348. Springer International Publishing, 2021.
    10.1007/978-3-030-79150-6_27
  3. Rui Ribeiro; André Pilastri; Hugo Carvalho; Arthur Matta; Pedro José Pereira; Pedro Rocha; Marcelo Alves; Paulo Cortez. "An Intelligent Decision Support System for Production Planning in Garments Industry". 2021.
    10.1007/978-3-030-91608-4_37
  4. Luís Ferreira; André Pilastri; Vítor Sousa; Filipe Romano; Paulo Cortez. "Prediction of Maintenance Equipment Failures Using Automated Machine Learning". 2021.
    10.1007/978-3-030-91608-4_26
  5. Luís Ferreira; André Pilastri; Carlos Martins; Pedro Santos; Paulo Cortez. "A Scalable and Automated Machine Learning Framework to Support Risk Management". 2021.
    10.1007/978-3-030-71158-0_14
  6. Ferreira, Luís; Pilastri, André Luiz; Martins, Carlos; Santos, Pedro; Cortez, Paulo. "A scalable and automated machine learning framework to support risk management". Springer, 2021.
    10.1007/978-3-030-71158-0_14
  7. Rui Ribeiro; André Pilastri; Carla Moura; Filipe Rodrigues; Rita Rocha; José Morgado; Paulo Cortez. "Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features". 244-255. Springer International Publishing, 2020.
    10.1007/978-3-030-49186-4_21
  8. António João Silva; Paulo Cortez; André Pilastri. "Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples". 232-243. Springer International Publishing, 2020.
    10.1007/978-3-030-49186-4_20
Livro
  1. André Luiz Pilastri; João Manuel R. S. Tavares. Reconstruction Algorithms in Compressive Sensing: An Overview. 2016.
Tese / Dissertação
  1. Pilastri, André Luiz [UNESP]. "Análise de multirresolução baseada em polinômio potência de Sigmóide - Wavelet". Mestrado, 2012. http://hdl.handle.net/11449/89343.
Distinções

Prémio

2021 Best Paper Award - Prediction of Maintenance Equipment Failures Using Automated Machine Learning
International Conference on Intelligent Data Engineering and Automated Learning, Reino Unido
2021 Best Paper Award - Prediction of Maintenance Equipment Failures Using Automated Machine Learning
International Conference on Computational Science and Its Applications, Espanha