• 4 Citations
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Personal profile

Research profile

João Pereira is currently a PDEng Trainee in Data Science at Eindhoven University of Technology (TU/e), where he develops data science projects for industry. Most of the time he works on machine learning and is broadly interested in the fields of data science and artificial intelligence.
His interests consist of applying cutting-edge AI techniques to solve difficult problems raised in the context of real-world applications. João Pereira has been working on AI applications involving time series, text, images, and videos, in the context of areas like energy, healthcare, finance, manufacturing, and AI for social good.
Prior to joining TU/e as a PDEng trainee, João Pereira completed a M.Sc. degree in Electrical and Computer Engineering at Instituto Superior Técnico (University of Lisbon), where he did his master thesis on deep learning. Meanwhile, he developed applied research on deep learning within the Signal and Image Processing Group at the Institute for Systems and Robotics (ISR-Lisbon).

Education/Academic qualification

Master, Instituto Superior Técnico (IST), University of Lisbon


Fingerprint Dive into the research topics where João Cardoso Pereira is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Time series Engineering & Materials Science
Electrocardiography Engineering & Materials Science
Recurrent neural networks Engineering & Materials Science
learning Social Sciences
Solar energy Engineering & Materials Science
time series Social Sciences
Decoding Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2018 2019

  • 4 Citations
  • 2 Conference contribution
  • 1 Article
  • 1 Phd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)
3 Citations (Scopus)

Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention

Pereira, J. & Silveira, M., 2018, Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018. Wani, M. A., Sayed-Mouchaweh, M., Lughofer, E., Gama, J. & Kantardzic, M. (eds.). Piscataway: Institute of Electrical and Electronics Engineers, p. 1275-1282 8 p. 8614232

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Time series
Smart sensors
Solar energy
1 Citation (Scopus)

Learning representations from healthcare time series data for unsupervised anomaly detection

Pereira, J. & Silveira, M., 1 Apr 2019, 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). Piscataway: Institute of Electrical and Electronics Engineers, 7 p. 8679157

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Time series
Anomaly detection
Time series data

Unsupervised representation learning and anomaly detection in ECG sequences

Pereira, J. & Silveira, M., 2019, In : International Journal of Data Mining and Bioinformatics. 22, 4, p. 389-407 19 p.

Research output: Contribution to journalArticleAcademicpeer-review

time series
Time series
Recurrent neural networks

Unsupervised anomaly detection in time series data using deep learning

Pereira, J., 2018, Lisboa: Técnico Lisboa. 92 p.

Research output: ThesisPhd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)

Time series
Recurrent neural networks
Solar energy
Learning algorithms

Activities 2019 2019

  • 1 Keynote talk

On Deep Learning - An Overview

João Cardoso Pereira (Speaker)
15 Oct 201922 Oct 2019

Activity: Talk or presentation typesKeynote talkScientific

Press / Media