Research

Working Papers

  1. Lalor, J. P., Chakraborty, I., & Kanuri, V. Extracting Style from Social Media Content to Predict Engagement.
  2. Chen, Y., Costello, J., Lalor, J. P., & Guo, R. Advancing the Design of Reputation and Feedback Systems in Education: A Field Experiment on Multidimensional Ratings.
  3. Yang, Y., Duan, H., Abbasi, A., Lalor, J. P., & Tam, K. Y. Bias Ahead? A Unified Bias Analysis Framework for Transformer-Based Language Models. https://arxiv.org/abs/2311.10395
  4. Lalor, J. P., Angst, C., Somanchi, S., D’Arcy, J., & Nwanganga, F. Country-Level Distributed Decoupling: Using NLP and Machine Learning to Investigate Narrative Divergence Related to GDPR Enforcement across EU Countries.
  5. Meng, G., Zeng, Q., Lalor, J. P., & Yu, H. A Psychology-Based Unified Dynamic Framework for Curriculum Learning. https://arxiv.org/abs/2408.05326
  6. Lalor, J. P., & Qu, S. On the Production and Spread of News in a Digital Age.
  7. Li, W., Lalor, J. P., Chen, Y., & Kanuri, V. From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision.
  8. Mohlmann, M., Lalor, J. P., Son, Y., & Berente, N. Inflation in Reputation Systems? Newcomers, Veterans, and Socialization into a Platform Context.
  9. Prat, N., Lalor, J. P., & Abbasi, A. GALEA – Leveraging Generative Agents in Artifact Evaluation.

Journal Articles

  1. Chen, J., Lalor, J. P., Liu, W., Druhl, E., Granillo, E., Vimalananda, V. G., & Yu, H. (2019). Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance. Journal of Medical Internet Research, 21(3), e11990. https://doi.org/10.2196/11990
  2. Lalor, J. P., Wu, H., Mazor, K. M., & Yu, H. (2023). Evaluating the Efficacy of NoteAid on EHR Note Comprehension among US Veterans through Amazon Mechanical Turk. International Journal of Medical Informatics, 172, 105006. https://www.sciencedirect.com/science/article/abs/pii/S1386505623000230
  3. Lalor, J. P., & Rodriguez, P. (2023). py-irt: A Scalable Item Response Theory Library for Python. INFORMS Journal on Computing\Textsuperscripta,b, 35(1), 5–13. https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2022.1250
  4. Lalor, J. P., Levy, D. A., Jordan, H. S., Hu, W., Smirnova, J. K., & Yu, H. (2024). Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study. Journal of Medical Internet Research, 26, e49704. https://www.jmir.org/2024/1/e49704
  5. Lalor, J. P., Wu, H., Chen, L., Mazor, K. M., & Yu, H. (2018). ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation. Journal of Medical Internet Research, 20(4), e9380. https://www.jmir.org/2018/4/e139/
  6. Lalor, J. P., Hu, W., Tran, M., Wu, H., Mazor, K. M., & Yu, H. (2021). Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients. Journal of Medical Internet Research, 23(5), e26354. https://www.jmir.org/2021/5/e26354/
  7. Lalor, J. P., Woolf, B., & Yu, H. (2019). Improving Electronic Health Record Note Comprehension with Noteaid: Randomized Trial of Electronic Health Record Note Comprehension Interventions with Crowdsourced Workers. Journal of Medical Internet Research, 21(1), e10793. https://www.jmir.org/2019/1/e10793/
  8. Lalor, J. P., Abbasi, A., Oketch, K., Yang, Y., & Forsgren, N. (2024). Should Fairness Be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines. ACM Trans. Inf. Syst.\Textsuperscripta, 42(4), 99:1–99:41. https://doi.org/10.1145/3641276
  9. Levy, D. A., Jordan, H. S., Lalor, J. P., Smirnova, J. K., Hu, W., Liu, W., & Yu, H. (2024). Individual Factors That Affect Laypeople’s Understanding of Definitions of Medical Jargon. Health Policy and Technology, 13(6), 100932. https://doi.org/10.1016/j.hlpt.2024.100932
  10. Safadi, H., Lalor, J. P., & Berente, N. (2024). The Effect of Bots on Human Interaction in Online Communities. MIS Quarterly\Textsuperscripta,b, 48(3), 1279–1295. https://aisel.aisnet.org/misq/vol48/iss3/15/
  11. Wowak, K. D., Lalor, J. P., Somanchi, S., & Angst, C. M. (2023). Business Analytics in Healthcare: Past, Present, and Future Trends. Manufacturing & Service Operations Management\Textsuperscripta,b, 25(3), 975–995. https://doi.org/10.1287/msom.2023.1192
  12. Yang, Y., Lalor, J. P., Abbasi, A., & Zeng, D. D. (2025). Hierarchical Deep Document Model. IEEE Transactions on Knowledge and Data Engineering\Textsuperscripta, 37(1), 351–364. https://doi.org/10.1109/TKDE.2024.3487523

Conference Proceedings

  1. Abbasi, A., Dobolyi, D., Lalor, J. P., Netemeyer, R. G., Smith, K., & Yang, Y. (2021). Constructing a Psychometric Testbed for Fair Natural Language Processing. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\Textsuperscripta, 3748–3758. https://aclanthology.org/2021.emnlp-main.304/
  2. Berente, N., Lalor, J. P., Somanchi, S., & Abbasi, A. (2021). The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations. International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2021/gen_topics/gen_topics/10/
  3. Chen, J., Lalor, J. P., & Yu, H. (2018). Detecting Hypoglycemia Incidents from Patients’ Secure Messages.
  4. Chen, J., Lalor, J. P., & Yu, H. (2018). Detecting Hypoglycemia Incidents from Patients’ Secure Messages.
  5. Cook, R., Lalor, J. P., & Abbasi, A. (2025). No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks. Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics\Textsuperscripta.
  6. Duan, X., & Lalor, J. P. (2023). H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition. Conference on Information Systems and Technology (CIST). https://arxiv.org/abs/2311.11981
  7. Lalor, J. P., Yang, Y., Smith, K., Forsgren, N., & Abbasi, A. (2022). Benchmarking Intersectional Biases in NLP. Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics\Textsuperscripta.
  8. Lalor, J. P., Wu, H., & Yu, H. (2016). Building an Evaluation Scale Using Item Response Theory. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing\Textsuperscripta, 2016, 648. https://aclanthology.org/D16-1062/
  9. Lalor, J. P., & Yu, H. (2020). Dynamic Data Selection for Curriculum Learning via Ability Estimation. Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, 545. https://aclanthology.org/2020.findings-emnlp.48/
  10. Lalor, J. P., Wu, H., & Yu, H. (2019). Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing\Textsuperscripta, 2019, 4240. https://aclanthology.org/D19-1434/
  11. Lalor, J. P., Wu, H., Munkhdalai, T., & Yu, H. (2018). Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing\Textsuperscripta, 2018, 4711. https://aclanthology.org/D18-1500/
  12. Miller, C., Settle, A., & Lalor, J. P. (2015). Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls. Proceedings of the 16th Annual Conference on Information Technology Education. https://dl.acm.org/doi/10.1145/2808006.2808017
  13. Rodriguez, P., Barrow, J., Hoyle, A. M., Lalor, J. P., Jia, R., & Boyd-Graber, J. (2021). Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\Textsuperscripta, 4486–4503. https://aclanthology.org/2021.acl-long.346/
  14. Safadi, H., Lalor, J. P., & Berente, N. (2021). The Effect of Bots on Human Interaction in Online Communities. International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2021/ai_business/ai_business/1/
  15. Settle, A., Lalor, J. P., & Steinbach, T. (2015). A Computer Science Linked-Courses Learning Community. Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education, 123–128. https://dl.acm.org/doi/10.1145/2729094.2742621
  16. Settle, A., Lalor, J. P., & Steinbach, T. (2015). Evaluating a Linked-Courses Learning Community for Development Majors. Proceedings of the 16th Annual Conference on Information Technology Education, 127–132. https://dl.acm.org/doi/10.1145/2808006.2808031
  17. Settle, A., Lalor, J. P., & Steinbach, T. (2015). Reconsidering the Impact of CS1 on Novice Attitudes. Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 229–234. https://dl.acm.org/doi/10.1145/2676723.2677235

Other Research Outputs

  1. Chen, J., Lalor, J. P., & Yu, H. (2018). Detecting Hypoglycemia Incidents from Patients’ Secure Messages.
  2. Chen, J., Lalor, J. P., & Yu, H. (2018). Detecting Hypoglycemia Incidents from Patients’ Secure Messages.
  3. Cho, E., Xie, H., Lalor, J. P., Kumar, V., & Campbell, W. M. (2019). Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity. https://arxiv.org/abs/1910.04196
  4. Lalor, J. P., Berente, N., & Safadi, H. (2020). Bots versus Humans in Online Social Networks: A Study of Reddit Communities.
  5. Lalor, J. P., Wu, H., & Yu, H. (2017). CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability. Human Computation and Crowdsourcing (HCOMP). https://arxiv.org/abs/1702.08563v2
  6. Lalor, J. P., Wu, H., & Yu, H. (2019). Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting.
  7. Lalor, J. P., Hu, W., Tran, M., Mazor, K., & Yu, H. (2021). Does Defining Medical Jargon In A Community Hospital Setting Improve Comprehension?
  8. Lalor, J. P., Wu, H., Chen, L., Mazor, K., & Yu, H. (2017). Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory.
  9. Lalor, J. P., Wu, H., & Yu, H. (2019). Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds.
  10. Lalor, J. P., & Guo, H. (2020). Towards Measuring Algorithmic Interpretability.
  11. Lalor, J. P., & Guo, H. (2021). Measuring Algorithmic Interpretability.
  12. Lalor, J. P., Wu, H., & Yu, H. (2018). Modeling Difficulty to Understand Deep Learning Performance.
  13. Lalor, J. P. (2022). On-the-Fly Difficulty Estimation for Deep Neural Networks.
  14. Lalor, J. P., Wu, H., & Yu, H. (2018). Soft Label Memorization-Generalization for Natural Language Inference. https://arxiv.org/abs/1702.08563v3
  15. Li, W., Chen, Y., Zheng, S., Wang, L., & Lalor, J. P. (2024). Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis. Proceedings of the Ninth Workshop on Noisy and User-Generated Text (w-NUT 2024), 104–118. https://aclanthology.org/2024.wnut-1.10/
  16. Ma, M.-C., & Lalor, J. P. (2020). An Empirical Analysis of Human-Bot Interaction on Reddit. Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-NUT 2020), 101–106. https://doi.org/10.18653/v1/2020.wnut-1.14
  17. Munkhdalai, T., Lalor, J. P., & Yu, H. (2016). Citation Analysis with Neural Attention Models. https://aclanthology.org/W16-6109/
  18. Rodriguez, P., Htut, P. M., Lalor, J. P., & Sedoc, J. (2022). Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory. Proceedings of the Third Workshop on Insights from Negative Results in NLP, 100–112. https://aclanthology.org/2022.insights-1.14/