Same Chart, Different Story: Bias in Vision-Language Chart Interpretation
Rahman, M., Wu, H., Asgari, A., Hoque, E., & Seyyed-Kalantari, L. (2026). Same Chart, Different Story: Bias in Vision-Language Chart Interpretation. EMNLP 2026.
CV
Education
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
- M.S. in Jekyll, GitHub University, 2014
- B.S. in GitHub, GitHub University, 2012
Work experience
- Spring 2024: Academic Pages Collaborator
- GitHub University
- Duties includes: Updates and improvements to template
- Supervisor: The Users
- Fall 2015: Research Assistant
- GitHub University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
- Summer 2015: Research Assistant
- GitHub University
- Duties included: Tagging issues
- Supervisor: Professor Git
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Chen, C.-Y., Abulibdeh, R., Asgari, A., Ordóñez, S. A. C., Celi, L. A., Goode, D., Hamidi, H., McCague, N., Seyyed-Kalantari, L., Sounack, T., & Kuo, P.-C. (2026). Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types. Proceedings of the 9th International Conference on Medical Imaging with Deep Learning, PMLR 315, 4166–4181.
ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts
Gohari Sadr, N., Madhusudan, S., Asgari, A., Sajjad, H., Seyyed-Kalantari, L., & Emami, A. (2026). ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts. Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), 428–453.
Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models
Asgari, A., Wu, H., Naziri, A., Kolahdouzi, M., & Seyyed-Kalantari, L. (2026). "Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models." Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 16868–16933.
MAM: Multinomial Attention Masking for Foundation Models on Sparse Single-Cell RNA-seq Data
Naziri, A., Asgari, A., An, A., Sachlos, E., & Seyyed-Kalantari, L. (2026). MAM: Multinomial Attention Masking for Foundation Models on Sparse Single-Cell RNA-seq Data. Proceedings of the 7th Conference on Health, Inference, and Learning, PMLR 333, 295–311.
Responsible Foundation Models for Healthcare: Risks, Opportunities, and Pathways to Responsibility
Seyyed-Kalantari, L., Kolahdouzi, M., Asgari, A., Hamidi, H., Naziri, A., Parkhimchyk, A., Tavakoli Afshari, S. M., Wu, H., Konate, S., & Mohanna, S. (2026). Responsible Foundation Models for Healthcare: Risks, Opportunities, and Pathways to Responsibility. In AI for a Just World: Power, Liberation, and the People Left Behind, Chapter 6, 72–81. Chapman & Hall/CRC.
Improving Classification of Cell Types in Acute Myeloid Leukemia with Self-guided Masking Technique
Naziri, A., Asgari, A., Sachlos, E., An, A., & Seyyed-Kalantari, L. (2025). Improving Classification of Cell Types in Acute Myeloid Leukemia with Self-guided Masking Technique. NeurIPS 2025 Workshop: AI Virtual Cells and Instruments: A New Era in Drug Discovery and Development.
MedPerturbing LLMs: A Comparative Study of Toxicity, Prompt Tuning, and Jailbreaks in Medical QA
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.
From Bias to Breakdown: Benchmarking Failure Mode Analysis of Single-cell RNA Sequencing Foundation Models in Acute Myeloid Leukemia
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.
Predicting the understandability of computational notebooks through code metrics analysis
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, 98.
DistilKaggle: a distilled dataset of Kaggle Jupyter notebooks
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.
Talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley, CA, USA
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, CA, USA
Teaching
Service and leadership
- Currently signed in to 43 different slack teams