Jean Paul Barddal

Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR).

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Office 8, Building 8

R. Imaculada Conceição, 1155

Curitiba, Paraná, Brazil

+55 41 3271-1351

I’m an Associate Professor with the Knowledge Discovery and Machine Learning (DCAM) of Graduate Program in Informatics (PPGIa) at the Pontifical Catholic University of Paraná (PUCPR). My research focuses on machine learning, more specifically on data stream mining. More specifically, I’m currently working on the following topics:

  • Supervised learning: data stream classification and regression
  • Unsupervised learning: data stream clustering and outlier detection
  • Data pre-processing: how to handle high-dimensionality, text data, etc, on streaming data
  • Distributed stream processing

If you’re also interested in any of the topics above, either in theoretical or applied ends, and you would like to pursue a degree or collaborate, feel free to drop me a message using the link below. Also, feel free to check my Lattes CV here (in Portuguese).

selected publications

  1. ACM TIST
    Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review
    Cristiano Mesquita Garcia, Ramon Abilio, Alessandro Lameiras Koerich, Alceu Souza Britto Jr., and Jean Paul Barddal
    ACM Transactions on Intelligent Systems and Technology 2024
  2. NEUCOM & APPS
    Representation ensemble learning applied to facial expression recognition
    Bruna Rossetto Delazeri, André Gustavo Hochuli, Jean Paul Barddal, Alessandro Lameiras Koerich, and Alceu Souza Britto Jr.
    Neural Computing and Applications 2024
  3. IEEE BIG DATA
    Is it Fine to Tune? Evaluating SentenceBERT Fine-tuning for Brazilian Portuguese Text Stream Classification
    Bruno Yuiti Leão Imai, Cristiano Mesquita Garcia, Marcio Vinicius Rocha, Alessandro Lameiras Koerich, Alceu Souza Britto Jr., and Jean Paul Barddal
    In IEEE International Conference on Big Data (IEEE Big Data) 2024
  4. IEEE BIG DATA
    LongKey: Keyphrase Extraction for Long Documents
    Jeovane Honorio Alves, Radu State, Cinthia Obladen Almendra Freitas, and Jean Paul Barddal
    In IEEE International Conference on Big Data (IEEE Big Data) 2024
  5. ICMLA
    Fuels Demand Forecasting: Identifying Leading Feature Sets, Prediction Strategy, and Regressors
    Jonas Krause, Alexandre C. A. Beiruth, Jean Paul Barddal, Alceu Souza Britto Jr, and Vinicius Mourão Alves Souza
    In International Conference on Machine Learning and Applications (ICMLA) 2024
  6. ICPR
    Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
    Israel A. Laurensi, Alceu Souza Britto Jr., Jean Paul Barddal, and Alessandro Lameiras Koerich
    In International Conference on Pattern Recognition (ICPR) 2024
  7. ICPR
    Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
    Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu Souza Britto Jr., and Jean Paul Barddal
    In International Conference on Pattern Recognition (ICPR) 2024
  8. ESWA
    Temporal analysis of drifting hashtags in textual data streams: A graph-based application
    Cristiano Mesquita Garcia, Alceu Souza Britto Jr., and Jean Paul Barddal
    Expert Systems with Applications 2024
  9. APL. SOFT. COMP.
    Adaptive Learning on Hierarchical Data Streams using Window-weighted Gaussian Probabilities
    Eduardo Tieppo, Julio Cesar Nievola, and Jean Paul Barddal
    Applied Soft Computing 2024
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