Journal Articles

  • Yi Yang, John P Lalor, Ahmed Abbasi, and Daniel Dajun Zeng. “Hierarchical Deep Document Model”. In: IEEE Transactions on Knowledge and Data Engineering 37.1 (Jan. 2025), pp. 351–364.
  • John P Lalor, Ahmed Abbasi, Kezia Oketch, Yi Yang, and Nicole Forsgren. “Should Fairness Be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines”. In: ACM Transactions on Information Systems 42.4 (Mar. 22, 2024), 99:1–99:41.
  • John P Lalor, David A Levy, Harmon S Jordan, Wen Hu, Jenni Kim Smirnova, and Hong Yu. “Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study”. In: Journal of Medical Internet Research 26 (2024), e49704.
  • David A Levy, Harmon S Jordan, John P Lalor, Jenni Kim Smirnova, Wen Hu, Weisong Liu, and Hong Yu. “Individual Factors That Affect Laypeople’s Understanding of Definitions of Medical Jargon”. In: Health Policy and Technology 13.6 (Dec. 1, 2024), p. 100932.
  • Hani Safadi, John P Lalor, and Nicholas Berente. “The Effect of Bots on Human Interaction in Online Communities”. In: MIS Quarterly 48.3 (2024), pp. 1279–1295.
  • John P Lalor and Pedro Rodriguez. “py-irt: A Scalable Item Response Theory Library for Python”. In: INFORMS Journal on Computing 35.1 (2023), pp. 5–13.
  • John P Lalor, Hao Wu, Kathleen M Mazor, and Hong Yu. “Evaluating the Efficacy of NoteAid on EHR Note Comprehension among US Veterans through Amazon Mechanical Turk”. In: International Journal of Medical Informatics 172 (2023), p. 105006.
  • Kaitlin D Wowak, John P Lalor, Sriram Somanchi, and Corey M Angst. “Business Analytics in Healthcare: Past, Present, and Future Trends”. In: Manufacturing & Service Operations Management 25.3 (May 2023), pp. 975–995.
  • John P Lalor, Wen Hu, Matthew Tran, Hao Wu, Kathleen M Mazor, and Hong Yu. “Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients”. In: Journal of Medical Internet Research 23.5 (2021), e26354.
  • Jinying Chen, John P Lalor, Weisong Liu, Emily Druhl, Edgard Granillo, Varsha G. Vimalananda, and Hong Yu. “Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance.” In: Journal of Medical Internet Research 21.3 (2019), e11990.
  • John P Lalor, Beverly Woolf, and Hong Yu. “Improving Electronic Health Record Note Comprehension with Noteaid: Randomized Trial of Electronic Health Record Note Comprehension Interventions with Crowdsourced Workers”. In: Journal of Medical Internet Research 21.1 (2019), e10793.
  • John P Lalor, Hao Wu, Li Chen, Kathleen M Mazor, and Hong Yu. “ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation”. In: Journal of Medical Internet Research 20.4 (2018), e9380.

Selected Proceedings

  • Ryan Cook, John P Lalor, and Ahmed Abbasi. “No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks”. In: Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics. 2025.
  • John P Lalor, Ruiyang Qin, David Dobolyi, and Ahmed Abbasi. “Textagon: Boosting Language Models with Theory-guided Parallel Representations”. In: Proceedings of the 2025 Annual Meeting of the Association for Computational Linguistics. 2025.
  • John P Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi. “Benchmarking Intersectional Biases in NLP”. In: Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2022.
  • Ahmed Abbasi, David Dobolyi, John P Lalor, Richard G Netemeyer, Kendall Smith, and Yi Yang. “Constructing a Psychometric Testbed for Fair Natural Language Processing”. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Authors listed alphabetically. 2021, pp. 3748–3758.
  • Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P Lalor, Robin Jia, and Jordan Boyd-Graber. “Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?” In: 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). 2021, pp. 4486–4503.
  • John P Lalor and Hong Yu. “Dynamic Data Selection for Curriculum Learning via Ability Estimation”. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Vol. 2020. 2020, p. 545.
  • John P Lalor, Hao Wu, and Hong Yu. “Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds”. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. Vol. 2019. 2019, p. 4240.
  • John P Lalor, Hao Wu, Tsendsuren Munkhdalai, and Hong Yu. “Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study”. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. Vol. 2018. 2018, p. 4711.
  • John P Lalor, Hao Wu, and Hong Yu. “Building an Evaluation Scale Using Item Response Theory”. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. Vol. 2016. 2016, p. 648.

Other Peer-Reviewed Presentations and Proceedings

  • Nicolas Prat, John P Lalor, and Ahmed Abbasi. “GALEA – Leveraging Generative Agents in Artifact Evaluation”. In: Proceedings of The 20th International Conference on Design Science Research in Information Systems and Technology (DESRIST). 2025.
  • Yi Yang, Hanyu Duan, Ahmed Abbasi, John P Lalor, and Kar Yan Tam. “Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads”. In: Proceedings of the Fifth Workshop on Trustworthy Natural Language Processing (TrustNLP). 2025.
  • John P Lalor, Corey Angst, Fred Nwanganga, and John D’Arcy. “It’s Not What You Say, It’s How You Say It: How Cultural Dimensions Impact GDPR Fine Summaries”. Twentieth Symposium on Statistical Challenges in Electronic Commerce Research. 2024.
  • John P Lalor, Corey Angst, Fred Nwanganga, and John D’Arcy. “It’s Not What You Say, It’s How You Say It: How Cultural Dimensions Impact GDPR Fine Summaries”. Academy of Management Annual Meeting. 2024.
  • John P Lalor, Pedro Rodriguez, João Sedoc, and Jose Hernandez-Orallo. “Item Response Theory for Natural Language Processing”. In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. Ed. by Mohsen Mesgar and Sharid Loáiciga. St. Julian’s, Malta: Association for Computational Linguistics, Mar. 2024, pp. 9–13.
  • Wenchang Li, Yixing Chen, Shuang Zheng, Lei Wang, and John P Lalor. “Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis”. In: Proceedings of the Ninth Workshop on Noisy and User-Generated Text (w-NUT 2024). 2024, pp. 104–118.
  • Xiaojing Duan and John P Lalor. “H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition”. In: Conference on Information Systems and Technology (CIST). 2023.
  • John P Lalor. “Ranking Pull Requests in Open Source Software”. Academy of Management Annual Meeting. 2023.
  • John P Lalor. “On-the-Fly Difficulty Estimation for Deep Neural Networks”. INFORMS Annual Meeting. 2022.
  • Pedro Rodriguez, Phu Mon Htut, John P Lalor, and João Sedoc. “Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory”. In: Proceedings of the Third Workshop on Insights from Negative Results in NLP. 2022, pp. 100–112.
  • Nicholas Berente, John P Lalor, Sriram Somanchi, and Ahmed Abbasi. “The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations”. In: International Conference on Information Systems (ICIS). 2021.
  • John P Lalor and Hong Guo. “Measuring Algorithmic Interpretability”. INFORMS Annual Meeting. 2021.
  • John P Lalor, Wen Hu, Matthew Tran, Kathleen Mazor, and Hong Yu. “Does Defining Medical Jargon In A Community Hospital Setting Improve Comprehension?” INFORMS Healthcare Conference. 2021.
  • Hani Safadi, John P Lalor, and Nicholas Berente. “The Effect of Bots on Human Interaction in Online Communities”. In: International Conference on Information Systems (ICIS). 2021.
  • John P Lalor, Nicholas Berente, and Hani Safadi. “Bots versus Humans in Online Social Networks: A Study of Reddit Communities”. INSNA Sunbelt Conference. 2020.
  • John P Lalor and Hong Guo. “Towards Measuring Algorithmic Interpretability”. INFORMS Workshop on Data Science. 2020.
  • Ming-Cheng Ma and John P Lalor. “An Empirical Analysis of Human-Bot Interaction on Reddit”. In: Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). EMNLP-WNUT 2020. Workshop on Noisy User-generated Text (W-NUT). Online: Association for Computational Linguistics, Nov. 2020, pp. 101–106.
  • Eunah Cho, He Xie, John P Lalor, Varun Kumar, and William M Campbell. “Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity”. ASRU 2019: The IEEE Automatic Speech Recognition and Understanding Workshop. 2019.
  • John P Lalor, Hao Wu, and Hong Yu. “Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting”. Workshop on Cognitive Modeling and Computational Linguistics (CMCL). 2019.
  • John P Lalor, Hao Wu, and Hong Yu. “Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds”. Workshop on Shortcomings in Vision and Language (SiVL). 2019.
  • Jinying Chen, John P Lalor, and Hong Yu. “Detecting Hypoglycemia Incidents from Patients’ Secure Messages”. American Medical Informatics Association (AMIA) Annual Symposium. 2018.
  • John P Lalor, Hao Wu, and Hong Yu. “Modeling Difficulty to Understand Deep Learning Performance”. Northern Lights Deep Learning Workshop (NLDL). 2018.
  • John P Lalor, Hao Wu, and Hong Yu. “Soft Label Memorization-Generalization for Natural Language Inference”. UAI Workshop on Uncertainty in Deep Learning. 2018.
  • John P Lalor, Hao Wu, Li Chen, Kathleen Mazor, and Hong Yu. “Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory”. American Medical Informatics Association (AMIA) Annual Symposium. 2017.
  • John P Lalor, Hao Wu, and Hong Yu. “CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability”. In: Human Computation and Crowdsourcing (HCOMP). 2017.
  • Tsendsuren Munkhdalai, John P Lalor, and Hong Yu. “Citation analysis with neural attention models”. In: Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis. 2016, pp. 69–77.
  • Craig Miller, Amber Settle, and John P Lalor. “Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls”. In: Proceedings of the 16th Annual Conference on Information Technology Education. 2015.
  • Amber Settle, John P Lalor, and Theresa Steinbach. “A Computer Science Linked-Courses Learning Community”. In: Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education. 2015, pp. 123–128.
  • Amber Settle, John P Lalor, and Theresa Steinbach. “Evaluating a Linked-Courses Learning Community for Development Majors”. In: Proceedings of the 16th Annual Conference on Information Technology Education. 2015, pp. 127–132.
  • Amber Settle, John P Lalor, and Theresa Steinbach. “Reconsidering the Impact of CS1 on Novice Attitudes”. In: Proceedings of the 46th ACM Technical Symposium on Computer Science Education. 2015, pp. 229–234.

Under Review/Revision

  • Sihan Chen, John P Lalor, Yi Yang, and Ahmed Abbasi. PersonaTwin: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins. Under review at ACL 2025.
  • John P Lalor, Corey Angst, Sriram Somanchi, John D’Arcy, and Fred Nwanganga. When Uniform Regulation Meets Local Realities: A Theory of Distributed Decoupling in the Case of GDPR and Empirical Validation. Major revision (after 1st round) at MIS Quarterly.
  • John P Lalor, Ishita Chakraborty, and Vamsi Kanuri. Extracting Style from Social Media Content to Predict Engagement. Reject and resubmit at Journal of Marketing Research.
  • Wenchang Li, John P Lalor, Yixing Chen, and Vamsi Kanuri. From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision. Under review (2nd round) at ACM Transactions on Management Information Systems.
  • Guangyu Meng, Qingkai Zeng, John P Lalor, and Hong Yu. A Psychology-Based Unified Dynamic Framework for Curriculum Learning. Major revision (after 1st round) at Computational Linguistics.
  • Mareike Mohlmann, John P Lalor, Yoon Son, and Nicholas Berente. Inflation in Reputation Systems? Newcomers, Veterans, and Socialization into a Platform Context. Major revision (after 3rd round) at Information Systems Research.
  • Kezia Oketch, John P Lalor, Yi Yang, and Ahmed Abbasi. Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open Models for Automated Essay Scoring. Under review at GEM2 Workshop: Generation, Evaluation & Metrics - ACL 2025.

Working Papers

  • Yixing Chen∗, John Costello∗, John P Lalor, and Robert Guo. Advancing the Design of Reputation and Feedback Systems in Education: A Field Experiment on Multidimensional Ratings.
  • John P Lalor, Hong Guo, Nicholas Berente, Ahmed Abbasi, and Jan Recker. Measuring Algorithmic Interpretability: A Human-Learning-Based Framework and the Corresponding Cognitive Complexity Score.
  • John P Lalor and Shawn Qu. On the Production and Spread of News in a Digital Age.
  • Shaochun Li, Ahmed Abbasi, Faizan Ahmad, John P Lalor, and Nitesh Chawla. MoveCast: Modeling Spatio-Temporal Movements Using Graph Neural Networks.
  • Zifeng Zhao, Shawn Qu, John P Lalor, and Ahmed Abbasi. Learning from the Curve: Predicting Successful Projects Using Functional PCA.
  • Shuang Zheng, John P Lalor, Yixing Chen, and Lei Wang. The Matthew Effect in Recommender Systems: Dynamics, Methodology, and Impact.
  • Ryan Cook, John P Lalor, and Ahmed Abbasi. CADE: Classification with Automatic Difficulty Estimation.
  • Kezia Oketch, John P Lalor, and Ahmed Abbasi. Is Linguistic Variation Signal or Noise? A Taxonomy-Guided Evaluation of Sociolinguistic Diversity in Swahili NLP.
  • Jung Hoon Lim, Sunjae Kwon, Zonghai Yao, John P Lalor, and Hong Yu. Large Language Model-Based Role-Playing for Personalized Medical Jargon Extraction.
  • John P Lalor and Rene Just. Ranking Pull Requests in Open Source Software.