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Research output · Synthetic data · Applied machine learning

Publications and research contributions

Selected journal articles, conference papers, and research work by Samer El Kababji, lead of Euler Letters AI.

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Journal Articles

  1. Huet-Dastarac, M., Dankar, F. K., Liu, D., El Kababji, S., Pilgram, L., & El Emam, K. (2026). An evaluation of pretrained generative models for augmenting small health data: Comparative modeling study. Journal of Medical Internet Research, 28, e88678. https://doi.org/10.2196/88678
  2. El Kababji, S., Mitsakakis, N., Jonker, E., Beltran-Bless, A.-A., Pond, G., Vandermeer, L., Radhakrishnan, D., Mosquera, L., Paterson, A., Shepherd, L., Chen, B., Barlow, W., Gralow, J., Savard, M.-F., Fesl, C., Hlauschek, D., Balic, M., Rinnerthaler, G., Greil, R., Gnant, M., Clemons, M., & El Emam, K. (2025). Augmenting insufficiently accruing oncology clinical trials using generative models: Validation study. Journal of Medical Internet Research, 27, e66821. https://doi.org/10.2196/66821
  3. Pilgram, L., El Kababji, S., Liu, D., & El Emam, K. (2025). Magnitude and impact of hallucinations in tabular synthetic health data on prognostic machine learning models: Validation study. Journal of Medical Internet Research, 27, e77893. https://doi.org/10.2196/77893
  4. Liu, D., El Kababji, S. E., Mitsakakis, N., Pilgram, L., Walters, T. D., Clemons, M., Pond, G. R., El-Hussuna, A., & El Emam, K. (2025). Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models. BMC Medical Informatics and Decision Making, 25(1). https://doi.org/10.1186/s12911-025-03266-3
  5. Mosquera, L., El Emam, K., Ding, L., Sharma, V., Zhang, X. H., El Kababji, S., Carvalho, C., Hamilton, B., Palfrey, D., Kong, L., Jiang, B., & Eurich, D. T. (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1). https://doi.org/10.1186/s12874-023-01869-w
  6. El Kababji, S., Mitsakakis, N., Fang, X., Beltran-Bless, A.-A., Pond, G. R., Vandermeer, L., Radhakrishnan, D., Mosquera, L., Clemons, M., & El Emam, K. (2023). Can synthetic data accurately mimic oncology clinical trials? Journal of Clinical Oncology, 41(16_suppl), 1554. https://doi.org/10.1200/jco.2023.41.16_suppl.1554
  7. El Kababji, S., Mitsakakis, N., Fang, X., Beltran-Bless, A.-A., Pond, G., Vandermeer, L., Radhakrishnan, D., Mosquera, L., Paterson, A., Shepherd, L., Chen, B., Barlow, W. E., Gralow, J., Savard, M.-F., Clemons, M., & El Emam, K. (2023). Evaluating the utility and privacy of synthetic breast cancer clinical trial data sets. JCO Clinical Cancer Informatics, 7, e2300116.
  8. El Kababji, S. E., & Srikantha, P. (2020). A data-driven approach for generating synthetic load patterns and usage habits. IEEE Transactions on Smart Grid, 11(6), 4984-4995.

Conference & Workshop Papers

  1. Naeini, A., El Kababji, S., & Srikantha, P. (2022). Reconstruction of grid measurements in the presence of adversarial attacks. NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning.
  2. Wang, J., El Kababji, S., Graham, C., & Srikantha, P. (2019). Ensemble-based deep learning model for non-intrusive load monitoring. IEEE Electrical Power and Energy Conference (EPEC). https://doi.org/10.1109/epec47565.2019.9074816
  3. El Kababji, S., & Srikantha, P. (2018). Power appliance disaggregation framework via hybrid hidden Markov model. IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). https://doi.org/10.1109/ccece.2018.8447822

Other Research Work

  1. El Kababji, S. (2021). Generative learning in smart grid. Doctoral thesis, Western University.