Yiting Tsai

Yiting Tsai

Yiting has obtained both his BASc and MASc degrees at CHBE. His current PhD research is focused on feature extraction, the identification of combined raw input variables which contribute to various process outcomes. This is achieved using Machine Learning algorithms such as Deep Learning, Variational Autoencoders, and Generative Adversarial Networks. Yiting is also working as a part-time data analyst in a consulting company, where he uses his fluency in Python to design predictive models to forecast and diagnose anomalies for real-time process data.

📚 Program/Degree
GROUP ALUMNI (2023) | PhD | Started 2016
📝 Research
Data-based Deep Optimal Feature Learning
📨 Contact

DAIS Lab Publications

  1. Conference Proceedings Keynote Presentation
    An Algorithm for Optimal Charging of Li-ion Batteries Using a Single Particle Model
    In Proceedings of the 5th International Symposium on Advanced Control of Industrial Processes, Hiroshima, Japan. 2014
  2. Conference Proceedings
    A novel algorithm for model-plant mismatch detection for model predictive controllers
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    In proceedings of International Symposium on Advanced Control of Chemical Processes (ADCHEM), Whistler, Canada.
    pp. 747 – 753. 2015
  3. Journal Paper
    State of charge estimation in Li-ion batteries: A particle filter approach
    , , ,
    Journal of Power Sources, 331.
    pp. 208 – 223. 2016
  4. Conference Proceedings Invited Paper
    Pattern and Knowledge Extraction using Process Data Analytics: A Tutorial
    , , , , ,
    In Proceedings of the 10th ADCHEM Conference, Shengyang, China.
    pp. 13-18. 2018
  5. Journal Paper
    A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance
    , , ,
    Processes. 2019 [PDF] [Code]
  6. Journal Paper
    Identifying Indicator Species in Ecological Habitats using Deep Optimal Feature Learning
    , ,
    PLOS ONE. 2021 [PDF] [Code]
  7. Journal Paper
    Targeted deep learning classification and feature extraction for clinical diagnosis
    , , , , ,
    iScience. 2023 [PDF]

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