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publications

Ontology Alignment Using Inductive Logic Programming

Published in International Conference on Web Research (ICWR), IEEE, 2018

Mapping ontologies using inverse resolution in inductive logic programming.

Recommended Citation: Karimi, H., & Kamandi, A. (2018). “Ontology Alignment Using Inductive Logic Programming.” In 4th International Conference on Web Research (ICWR), 118–127. IEEE.
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YAD: A Learning-based Inductive Logic Programming Tool

Published in Journal of Open Source Software (JOSS), 2018

An open-source, Inductive Logic Programming tool for multi-tabular and multi-dimensional learning based on inverse resolution.

Recommended Citation: Kamandi, A., & Karimi, H. (2018). “YAD: A Learning-based Inductive Logic Programming Tool.” Journal of Open Source Software (JOSS), 3(30), 892.
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Application-Based Review of Recent Advances of Data Mining in Healthcare

Published in Journal of Biostatistics and Epidemiology (JBE), 2019

Review of data mining advances in healthcare with an application-based perspective.

Recommended Citation: Shirazi, S., Baziyad, H., & Karimi, H. (2019). “An Application-Based Review of Recent Advances of Data Mining in Healthcare.” Journal of Biostatistics and Epidemiology, 5(4).
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A Learning-based Ontology Alignment Approach Using Inductive Logic Programming

Published in Journal of Expert Systems with Applications (ESWA), Elsevier, 2019

Ontology alignment via learning with Inductive Logic Programming (ILP).

Recommended Citation: Karimi, H., & Kamandi, A. (2019). “A Learning-based Ontology Alignment Approach Using Inductive Logic Programming.” Journal of Expert Systems with Applications (ESWA), Elsevier, 125(C), 412–424.
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Quantifying Deep Learning Model Uncertainty in Conformal Prediction

Published in AAAI Symposium Series (Human AI), 2023

Probabilistic quantification of uncertainty derived from conformal prediction sets.

Recommended Citation: Karimi, H., & Samavi, R. (2023). “Quantifying Deep Learning Model Uncertainty in Conformal Prediction.” Proceedings of the AAAI Symposium Series, 1(1), 142–148.
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DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks

Published in Asian Conference on Machine Learning (ACML) - PMLR, 2024

Two-phase training with diversified ensembles and noisy logits for improved adversarial robustness.

Recommended Citation: Yazdani, M., Karimi, H., & Samavi, R. (2024). “DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks.” In Proceedings of the 15th Asian Conference on Machine Learning. Proceedings of Machine Learning Research (PMLR), vol. 222, 1574–1589.
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Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction

Published in Symposium on Conformal and Probabilistic Prediction with Applications (COPA), PMLR, 2024

Evidential Conformal Prediction (ECP) for adaptive prediction sets with coverage guarantees.

Recommended Citation: Karimi, H., & Samavi, R. (2024). “Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction.” In Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications. Proceedings of Machine Learning Research (PMLR), vol. 230, 466–489.
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SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation

Published in Medical Imaging with Deep Learning (MIDL), PMLR, 2025

Spatially-adaptive conformal prediction for anatomically informed uncertainty sets in medical image segmentation.

Recommended Citation: Bereska, J. I., Karimi, H., & Samavi, R. (2025). “SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation.” In Medical Imaging with Deep Learning (MIDL 2025). Published in Proceedings of Machine Learning Research (PMLR).
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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015