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Zhendong Wang

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Postdoc

Details

Works for

Division of Information Science and Engineering

Unit address
Malvinas Väg 10
E-mail
zhwang@kth.se
Room
A:419

Researcher

Researcher ID
Google Scholar ID

About me

Postdoctoral Researcher at the School of Electrical Engineering and Computer Science (EECS) of KTH Royal Institute of Technology. Affiliated to Björn Nordlund's research group at the Department of Women's and Children's Health of Karolinska Institutet (KI).

Research Interests:

  • EXplainable Artificial Intelligence (XAI)

  • Temporal data mining

  • Machine learning for healthcare

Teaching Activities:

  • Thesis supervision:

    • 1 Master's (MSc) thesis (ongoing), EECS, KTH Royal Institute of Technology (2025-Present).

    • 6 Master's (MSc) theses and 3 Bachelor's (BSc) theses, DSV, Stockholm University (2022-2024).

  • Guest lectured on ML Interpretability (Counterfactual Explanations) for the Research Topics in Data Science (DAMI II) course, DSV, Stockholm University (HT2023/HT2024).

  • Teaching assistant:

    • Research Topics in Data Science (DAMI II), DSV, Stockholm University (2022-2024).

    • Introduction to Machine Learning (732A95), Linköping University (2017-2018).

    • Neural Network and Learning Systems (TBMI26), Linköping University (2017-2018).

    • Computational Statistics (732A90), Linköping University (2017-2018).

Research Projects:

  • A3S: AI-based Asthma App using Spirometer (2025-Present).

  • EXTREMUM: Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources (2020-2024).

Recent Publications:

  • Z. Wang, M. Jansson, S. Chatterjee, H Ljungberg, L. Myers, I. Miliou, B. Nordlund, "Utilising real-world temporal home spirometry data for predicting asthma exacerbations", European Respiratory Congress (European Respiratory Journal - Late Breaking Abstract), vol. 66, no. suppl 69, Nov. 2025, doi: 10.1183/13993003.congress-2025.PA2041.

  • D. G. Pérez,Z. Wang, and J. M. E. González, “CACTUS: A Context-Aware Framework for Counterfactual Explanations Across Diverse Prediction Domains,” in Discovery Science, Cham, 2025, pp. 460–475. doi: 10.1007/978-3-032-05461-6_30.

  • Z. Wang, I. Samsten, I. Miliou, R. Mochaourab, and P. Papapetrou, “Glacier: guided locally constrained counterfactual explanations for time series classification”, Machine Learning, vol. 113, no. 3, Mar. 2024, doi: 10.1007/s10994-023-06502-x.

  • Z. Wang, I. Samsten, I. Miliou, and P. Papapetrou, “COMET: Constrained Counterfactual Explanations for Patient Glucose Multivariate Forecasting,” in 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2024, pp. 502–507. doi: 10.1109/CBMS61543.2024.00089.

  • Z. Wang, I. Miliou, I. Samsten, and P. Papapetrou, “Counterfactual Explanations for Time Series Forecasting,” in 2023 IEEE International Conference on Data Mining (ICDM), Dec. 2023, pp. 1391–1396. doi: 10.1109/ICDM58522.2023.00180.

  • Z. Wang, I. Samsten, V. Kougia, and P. Papapetrou, ‘Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients’, Artificial Intelligence in Medicine, p. 102457, Nov. 2022, doi: 10.1016/j.artmed.2022.102457.

  • Z. Wang, I. Samsten, R. Mochaourab, and P. Papapetrou, ‘Learning Time Series Counterfactuals via Latent Space Representations’, in Discovery Science, Cham, 2021, pp. 369–384. DOI: 10.1007/978-3-030-88942-5_29. PDF preview: https://rdcu.be/cze5Y.

  • Z. Wang, I. Samsten, and P. Papapetrou, ‘Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients’,in Artificial Intelligence in Medicine, Cham, 2021, pp. 338–348. DOI: 10.1007/978-3-030-77211-6_38. PDF preview: https://rdcu.be/cmfbO. (Best Student Paper Award)

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