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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
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
DistilKaggle: a distilled dataset of Kaggle Jupyter notebooks
Published in Proceedings of the 21st International Conference on Mining Software Repositories, 2024
DistilKaggle introduces a rigorously curated dataset of Kaggle Jupyter notebooks designed to support empirical studies and machine learning research.
Recommended citation: Mostafavi Ghahfarokhi, M., Asgari, A., Abolnejadian, M., & Heydarnoori, A. (2024). "DistilKaggle: a distilled dataset of Kaggle Jupyter notebooks." Proceedings of the 21st International Conference on Mining Software Repositories, 647-651.
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Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Published in arXiv preprint, 2025
This research demonstrates that machine learning algorithms trained solely on normal chest X-rays can inadvertently learn to predict a patient`s health insurance type.
Recommended citation: Chen, C.-Y., Abulibdeh, R., Asgari, A., Ordóñez, S. A. C., Celi, L. A., Goode, D., ... & Seyyed-Kalantari, L. (2025). "Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types." arXiv preprint arXiv:2511.11030.
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Predicting the understandability of computational notebooks through code metrics analysis
Published in Empirical Software Engineering, 2025
This paper investigates how various code metrics can be utilized to evaluate and predict the understandability of computational notebooks.
Recommended citation: Ghahfarokhi, M. M., Asadi, A., Asgari, A., Mohammadi, B., & Heydarnoori, A. (2025). "Predicting the understandability of computational notebooks through code metrics analysis." Empirical Software Engineering, 30(4), 98.
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From Bias to Breakdown: Benchmarking Failure Mode Analysis of Single-cell RNA Sequencing Foundation Models in Acute Myeloid Leukemia
Published in Proceedings of the AAAI Symposium Series, 2025
This study provides a comprehensive benchmarking of failure modes in foundation models applied to single-cell RNA sequencing data for Acute Myeloid Leukemia.
Recommended citation: Naziri, A., Asgari, A., An, A., Sachlos, E., & Seyyed-Kalantari, L. (2025). "From Bias to Breakdown: Benchmarking Failure Mode Analysis of Single-cell RNA Sequencing Foundation Models in Acute Myeloid Leukemia." Proceedings of the AAAI Symposium Series, 7(1), 553-557.
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MedPerturbing LLMs: A Comparative Study of Toxicity, Prompt Tuning, and Jailbreaks in Medical QA
Published in Proceedings of the AAAI Symposium Series, 2025
This comparative study evaluates the robustness and safety of Large Language Models (LLMs) in the context of medical question-answering.
Recommended citation: Asgari, A., Naziri, A., & Seyyed-Kalantari, L. (2025). "MedPerturbing LLMs: A Comparative Study of Toxicity, Prompt Tuning, and Jailbreaks in Medical QA." Proceedings of the AAAI Symposium Series, 7(1), 438-447.
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Improving Classification of Cell Types in Acute Myeloid Leukemia with Self-guided Masking
Published in Preprint, 2026
This research presents a novel computational approach utilizing self-guided masking techniques to enhance the classification of cell types in Acute Myeloid Leukemia.
Recommended citation: Naziri, A., Asgari, A., Sachlos, E., An, A., & Seyyed-Kalantari, L. (2024). "Improving Classification of Cell Types in Acute Myeloid Leukemia with Self-guided Masking." Preprint.
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Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models
Published in The 64th Annual Meeting of the Association for Computational Linguistics (ACL), 2026
This paper presents a comprehensive study quantifying the agreement across various bias evaluation metrics and Large Language Models (LLMs), aiming to provide a more robust understanding of fairness assessments in natural language processing.
Recommended citation: Asgari, A., Wu, H., Naziri, A., Kolahdouzi, M., & Seyyed-Kalantari, L. (2026). "Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models." The 64th Annual Meeting of the Association for Computational Linguistics.
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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
This is a description of a teaching experience. You can use markdown like any other post.