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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.
Identification

Personal identification

Full name
Andre Pilastri

Citation names

  • Pilastri, Andre

Author identifiers

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

Email addresses

  • andre.pilastri@ccg.pt (Professional)

Websites

Knowledge fields

  • Exact Sciences - Computer and Information Sciences - Computer Sciences

Languages

Language Speaking Reading Writing Listening Peer-review
Portuguese (Mother tongue)
English Intermediate (B1) Advanced (C1) Advanced (C1) Upper intermediate (B2) Advanced (C1)
Education
Degree Classification
2017
Ongoing
 Engenharia Informática (Doutoramento)
Major in Sem especialidade
Universidade do Porto Faculdade de Engenharia, Portugal
"Redes Complexas em Visão Computacional - Aplicação na Análise de Imagens Dermatoscópicas" (THESIS/DISSERTATION)
Outputs

Publications

Book
  1. André Luiz Pilastri; João Manuel R. S. Tavares. Reconstruction Algorithms in Compressive Sensing: An Overview. 2016.
Book chapter
  1. Cláudia Afonso; Arthur Matta; Luís Miguel Matos; Miguel Bastos Gomes; Antonina Santos; André Pilastri; Paulo Cortez. "Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case". 2023.
    10.1007/978-3-031-34107-6_27
  2. Ferreira, César; Fertuzinhos, Aureliano; Silva, Ricardo; Ramalho, Miguel; Vale, Bruno; Silva, João; Costa, Luani; et al. "Powered Smart Textile-Based Exoskeleton for Human Support Movement". In Proceedings of the 9th International Ergonomics Conference, 113-121. Springer Nature Switzerland, 2023.
    10.1007/978-3-031-33986-8_13
  3. Afonso Sousa; Luís Ferreira; Rui Ribeiro; João Xavier; André Pilastri; Paulo Cortez. "Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning". 2022.
    10.1007/978-3-031-08337-2_22
  4. Gonçalo Fontes; Luís Miguel Matos; Arthur Matta; André Pilastri; Paulo Cortez. "An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets". 2022.
    10.1007/978-3-031-08333-4_7
  5. Pereira, Pedro José; Pereira, Adriana; Cortez, Paulo; Pilastri, André. "A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data". In Progress in Artificial Intelligence, 561-572. Springer International Publishing, 2021.
  6. 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.
  7. 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
  8. 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
  9. Luís Ferreira; André Pilastri; Carlos Martins; Pedro Santos; Paulo Cortez. "A Scalable and Automated Machine Learning Framework to Support Risk Management". 2021.
  10. 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
  11. 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
  12. 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
Conference paper
  1. Pedro José Pereira; Carlos Gonçalves; Lara Lopes Nunes; Paulo Cortez; André Pilastri. "AI4CITY - An Automated Machine Learning Platform for Smart Cities". 2023.
    10.1145/3555776.3578740
  2. João Nuno Oliveira; Luani Costa; Ana Ramôa; Ricardo Silva; Aureliano Fertuzinhos; Bruno Vale; Inês Estudante; et al. "Worker 4.0: A Textile Exoskeleton to Support Apparel Industry". 2023.
    10.54941/ahfe1003636
  3. Fernandes, Cristiana; Matos, Luis Miguel; Folgado, Duarte; Nunes, Maria Lua; Pereira, Joao Rui; Pilastri, Andre; Cortez, Paulo. "A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors". 2022.
    10.1109/IJCNN55064.2022.9892409
  4. Macedo, Luisa; Matos, Luis Miguel; Cortez, Paulo; Domingues, Andre; Moreira, Guilherme; Pilastri, Andre. "A Machine Learning Approach for Spare Parts Lifetime Estimation". 2022.
    10.5220/0010903800003116
  5. Ferreira, Luis; Silva, Leopoldo; Pinho, Diana; Morais, Francisco; Martins, Carlos Manuel; Pires, Pedro Miguel; Fidalgo, Pedro; et al. "A Federated Machine Learning Approach to Detect International Revenue Share Fraud on the 5G Edge". 2022.
    10.1145/3477314.3507322
  6. Pilastri, Andre. "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost". 2021.
    10.1109/ijcnn52387.2021.9534091
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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.
  13. Pilastri, Andre. "Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology". 2020.
    10.5220/0009411205480555
  14. Pilastri, Andre. "An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management". 2020.
    10.5220/0008952800990107
  15. 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
  16. 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
  17. 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
  18. 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
  19. Andre Pilastri; João Papa; João Manuel R. S. Tavares. "Segmentation of Skin in dermatoscopic images using SuperPixels combined with Complex Networks". 2018.
  20. 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
  21. 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
Journal article
  1. Paula Dias; Arthur Matta; André Pilastri; Luís Miguel Matos; Paulo Cortez. "RTSIMU: Real-Time Simulation tool for IMU sensors". Software Impacts 17 (2023): 100522-100522. http://dx.doi.org/10.1016/j.simpa.2023.100522.
    10.1016/j.simpa.2023.100522
  2. Rui Ribeiro; André Pilastri; Carla Moura; José Morgado; Paulo Cortez. "A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics". Neural Computing and Applications 35 23 (2023): 17375-17395. http://dx.doi.org/10.1007/s00521-023-08596-9.
    10.1007/s00521-023-08596-9
  3. Luís Ferreira; Leopoldo Silva; Francisco Morais; Carlos Manuel Martins; Pedro Miguel Pires; Helena Rodrigues; Paulo Cortez; André Pilastri. "International revenue share fraud prediction on the 5G edge using federated learning". Computing (2023): http://dx.doi.org/10.1007/s00607-023-01174-w.
    10.1007/s00607-023-01174-w
  4. Paulo Peças; Lenin John; Inês Ribeiro; António J. Baptista; Sara M. Pinto; Rui Dias; Juan Henriques; et al. "Holistic Framework to Data-Driven Sustainability Assessment". Sustainability (2023): https://doi.org/10.3390/su15043562.
    10.3390/su15043562
  5. Diogo Ribeiro; Luís Miguel Matos; Jose Guilherme Cruz Moreira; Andre Pilastri; Paulo Cortez. "Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection". Computers (2022): https://www.mdpi.com/2073-431X/11/4/54.
    10.3390/computers11040054
  6. Matos, Luis Miguel; Azevedo, Joao; Matta, Arthur; Pilastri, Andre; Cortez, Paulo; Mendes, Rui. "Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing". Software Impacts (2022): https://publons.com/wos-op/publon/53369258/.
    10.1016/J.SIMPA.2022.100359
  7. Carvalho, Hugo Silva; Pilastri, Andre; Novais, Rui; Cortez, Paulo. "Original publication RanCoord - A random geographic coordinates generator for transport and logistics research and development activities". Software Impacts (2022): https://publons.com/wos-op/publon/54849771/.
    10.1016/J.SIMPA.2022.100428
  8. João Azevedo; Rui Ribeiro; Luís Miguel Matos; Rui Sousa; João Paulo Silva; André Pilastri; Paulo Cortez. "Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach". Procedia Computer Science (2022): https://publons.com/wos-op/publon/54849788/.
    10.1016/J.PROCS.2022.09.289
  9. Coelho, Gabriel; Matos, Luis Miguel; Pereira, Pedro Jose; Ferreira, Andre; Pilastri, Andre; Cortez, Paulo. "Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio". Neural Computing and Applications (2022): https://publons.com/wos-op/publon/52578760/.
    10.1007/S00521-022-07375-2
  10. 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
  11. 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.
  12. 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
Thesis / Dissertation
  1. Pilastri, André Luiz [UNESP]. "Análise de multirresolução baseada em polinômio potência de Sigmóide - Wavelet". Master, 2012. http://hdl.handle.net/11449/89343.
Distinctions

Award

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