Lan Jiang
Doctoral Student
PhD, Information Sciences, Illinois (in progress)
MS, Illinois
BE, Sun Yat-sen University
Research focus
My research primarily focuses on natural language processing, with an emphasis on text assessments across various contexts, including education and biomedical domains. I am also interested in uncovering and eliminating bias in natural language processing to improve fairness.
Courses currently teaching
Advisors
Publications & Papers
Jiang, L., Lan, M., Menke, J. D., Vorland, C. J., & Kilicoglu, H. (2024). Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines. Scientific Reports, 14(1), 21721.
Jiang, L., & Bosch, N. (2024, July). Short answer scoring with GPT-4. In Proceedings of the Eleventh ACM Conference on Learning@ Scale (pp. 438-442).
Jiang, L., Belitz, C., & Bosch, N. (2024, March). Synthetic dataset generation for fairer unfairness research. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 200-209).
Kilicoglu, H., Jiang, L., Hoang, L., Mayo-Wilson, E., Vinkers, C. H., & Otte, W. M. (2023). Methodology reporting improved over time in 176,469 randomized controlled trials. Journal of Clinical Epidemiology, 162, 19-28.
Hoanga, L., Jiang, L., & Kilicoglu, H. (2022). Investigating the impact of weakly supervised data on text mining models of publication transparency: a case study on randomized controlled trials. AMIA Summits on Translational Science Proceedings, 2022, 254.
Belitz, C., Jiang, L., & Bosch, N. (2021, July). Automating procedurally fair feature selection in machine learning. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 379-389).
Jiang, L., & Bosch, N. (2024, July). Predictive sequential pattern mining via interpretable convolutional neural networks. In Proceedings of the 14th International Conference on Educational Data Mining (pp. 761-766).
Jiang, L., Dinh, L., Rezapour, R., & Diesner, J. (2021). Which group do you belong to? sentiment-based pagerank to measure formal and informal influence of nodes in networks. In Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020 (pp. 623-636). Springer International Publishing.