Pattarawat Chormai

Doctoral Candidate (deputy student representative)

Research interests: • deep learning and its interpretability • natural language processing • computational methods in sciences • knowledge acquisition and sampling-based decision-marking • data visualisation

Academic education

since 2019     Doctoral candidate, Max Planck School of Cognition, Leipzig
                         Doctoral research performed at Technical University of Berlin
                       Supervisors: Klaus-Robert Müller & Grégoire Montavon

2015–2018 Master of Science in Data Science, Eindhoven University of Technology, the Netherlands & Technical University of Berlin (dual degree program organized by EIT Digital Master School)

2008–2012 Bachelor of Science in Information Technology, King Mongkut's Institute of Technology, Ladkrabang, Thailand

Teaching experience

2020/2021 Machine Learning (exercise grading), Technical University of Berlin

Publications

Chormai, P., Prasertsom, P., Cheevaprawatdomrong, J., & Rutherford, A. (2020). Syllable-based neural Thai word segmentation. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain. International Committee on Computational Linguistics, 4619–4637. doi:10.18653/vl/2020.coling-main.407

Rieger, L., Chormai, P., Montavon, G., Hansen, L. K., & Müller, K.-R. (2018). Structuring neural networks for more explainable predictions. In H. J. Escalante et al. (Eds.), Explainable and interpretable models in computer vision and machine learning. The Springer series on challenges in machine learning (pp. 115–131). Springer: Cham, Switzerland.

Photo: Nikolaus Brade