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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
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Blog Post number 1
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portfolio
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publications
Ontology Alignment Using Inductive Logic Programming
Published in Proceedings of the International Conference on Web Research (ICWR), IEEE Xplore, 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 Proceedings of the AAAI Symposium Series (Human AI), 2023
Certified quantification of uncertainty derived from conformal prediction sets.
Recommended Citation: Karimi, H., & Samavi, R. (2023). “Quantifying Deep Learning Model Uncertainty in Conformal Prediction.” The Proceedings of the AAAI Symposium Series, 1(1), pp. 142–148.
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DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks
Published in Proceedings of the 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 Proceedings of the 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 Proceedings of 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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CEAR: Certified Ensemble Adversarial Robustness in DNNs
Published in Proceedings of the Canadian Conference on Artificial Intelligence (Canadian AI), 2026
An ensemble-based robust method that utilizes a hybrid of empirical and certified defense mechanisms.
Recommended Citation: Sadig, D., Maleki, M., Karimi, H., & Samavi, R. (2026). “CEAR: Certified Ensemble Adversarial Robustness in DNNs.” Accepted to be published in the 39th Canadian Conference on Artificial Intelligence (Canadian AI 2026).
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LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
Published in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2026
Uncertainty Quantification and adaptive accept/abstain decision rule in LLMs Using Conformal Semantic Entropy (ACSE).
Recommended Citation: Karimi, H., Meyappan, V., & Samavi, R. (2026). “LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy.” Accepted to be published in the Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI 2026).
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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