Hamed Karimi

Hamed Karimi

Ph.D. Candidate (Research Assistant) in Computer Engineering.
Working on Uncertainty Quantification in Trustworthy AI and Machine Learning.

Selected Publications

You can also find my research papers on my Google Scholar profile.

Conference Papers

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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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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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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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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Journal Articles

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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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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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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