Lynn Pickering

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Lynn Pickering, Dr.

Sepcially appointed assistant professor

Computational Intelligence Laboratory
Dept. of Core Informatics
Graduate School of Informatics
Osaka Metropolitan University

E-mail: pickerln (at) omu.ac.jp
Address: 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan
Phone: +81-72-254-9350
FAX: +81-72-254-9825



Lynn Pickering received the B.S. degree in aerospace engineering from the University of Cincinnati, Ohio, USA, in 2020 and the Ph.D. degree from the Department of Aerospace Engineering and Engineering Mechanics from the University of Cincinnati in 2025, advised by Dr. Bernard De Baets and Dr. Kelly Cohen. From April 2026, she has been a specially appointed assistant professor in the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University. Her research interests include explainable artificial intelligence (XAI), evolutionary fuzzy systems, and interpretability as a path towards AI that is safe and effective for human-centric applications.


2026.4- : Specially appointed assistant professor, Graduate School of Informatics, Osaka Metropolitan University, JAPAN

Awards
Research Stay


Book Chapters
  1. L. Pickering and K. Cohen, "Genetic Fuzzy Controller for the Homicidal Chauffeur Differential Game," in Applications of Fuzzy Techniques, Springer, pp. 196-204, 2023. [DOI]
  2. B. Courcier, S. Richard Desjardins, C. Farges, F. Cazaurang, K. Cohen, L. Pickering, and J. Viaña Perez, "Genetic Fuzzy System for Pitch Control on a F-4 Phantom," in Applications of Fuzzy Techniques, Springer, pp. 31-39, 2023. [DOI]
  3. L. Pickering and K. Cohen, "Towards Explainable AI - Genetic Fuzzy Systems - A Use Case," in Explainable AI and Other Applications of Fuzzy Techniques, Springer, pp. 343-354, 2021. [DOI]
  4. L. Pickering and K. Cohen, "Genetic Fuzzy Systems: Genetic Fuzzy Based Tetris Player," in Advances in Intelligent Systems and Computing, Springer, pp. 313-326, 2021. [DOI]

Journal Papers
  1. L. Pickering, K. Cohen, and B. De Baets, "A Narrative Review on the Interpretability of Fuzzy Rule-Based Models from a Modern Interpretable Machine Learning Perspective," International Journal of Fuzzy Systems, vol. 27, pp. 1-20, 2025. [DOI]
  2. L. Pickering, V. Ciulei, P. Merkx, J. van Vliet, and K. Cohen, "Evaluating the interpretability of a hierarchical fuzzy rule-based model for shipbreaking," Complex Engineering Systems, vol. 5, no. 4, Art. 16, Dec. 2025. [DOI]
  3. L. Pickering, T. del Río Almajano, M. England, and K. Cohen, "Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition," Journal of Symbolic Computation, vol. 123, 102276, 2024. [DOI]

Conference Papers

    2025
  1. L. Pickering, Y. Nojima, K. Cohen, and B. De Baets, "Using the lenses of interpretability for analyzing FRBMs trained on electronic health records," in Proc. of 2025 IFSA World Congress and NAFIPS Annual Conference, 6 pages, Alberta, Canada, Aug. 2025.

    2. 2023
  2. L. Pickering, V. Ciulei, P. Merkx, B. De Baets, and K. Cohen, "Training hierarchical fuzzy systems to predict shipbreaking and shipbeaching on real world ILT data," [abstract] in Book of Abstracts: EUSFLAT 2023, Palma, Spain, Sept. 2023.

    3. 2021
  3. L. Pickering, N. Ernest, T. Arnett, B. Kunkel, and J. Viana Perez, "Explainable AI Challenge – Student Competition," NAFIPS 2021, Purdue University, June 2021.

    4. 2020
  4. L. Pickering, J. Viana Perez, X. Li, A. Chhabra, D. Patel, and K. Cohen, "Identifying New Inputs in COVID - 19 AI Case Predictions," Proc. of 7th ISCMI 2020, pp. 192-196, Stockholm, Sweden, Nov. 2020. [DOI]
  5. A. Chhabra, D. Patel, J. Viana Perez, L. Pickering, X. Li, and K. Cohen, "Understanding the Effects of Human Factors on the Spread of COVID-19 using a Neural Network," Proc. of 7th ISCMI 2020, pp. 121-125, Stockholm, Sweden, Nov. 2020. [DOI]