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Dr Jacob Montiel

Postdoctoral Fellow (Computer Science)

Qualifications: PhD UPS-TPT

Contact Details

Email: jmontiel@waikato.ac.nz
Room: FG.2.04
Phone: +64 7 837 9641
Website: https://jacobmontiel.github.io/

About Jacob

I am a Research Fellow in the Machine Learning Group at the University of Waikato. I am the lead developer and maintainer of scikit-multiflow, a machine learning framework for multi-label/multi-output stream data.

I worked at GE Aviation as Tech Lead in the development of embedded software for Diagnostics and Prognostics of Airborne Systems.

Recent Publications

  • Montiel, J. (2020). Learning from evolving data streams. In Proceedings of the 19th Python in Science Conference (pp. 70-77). SciPy. doi:10.25080/majora-342d178e-00a

  • Yogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2020). Seeing the whole patient: Using multi-label medical text classification techniques to enhance predictions of medical codes.. CoRR, abs/2004.00430.

  • Togbe, M., Barry, M., Boly, A., Chabchoub, Y., Chiky, R., Montiel Lopez, J., & Vinh-Thuy, T. (2020). Anomaly detection for data streams based on isolation forest using scikit-multiflow. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, . . . C. M. Torre (Eds.), Proc 20th International Conference on Computational Science and its Applications (ICCSA 2020) Part IV, LNCS 12252 (pp. 15-30). Caligari, Italy. doi:10.1007/978-3-030-58811-3_2

  • Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., & Bifet, A. (2020). Adaptive XGBoost for evolving data streams. In Proc 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Glasgow, UK: IEEE. doi:10.1109/IJCNN48605.2020.9207555

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