About Me
I am a Ph.D. candidate at the University of Notre Dame, specializing in Quantitative Psychology (my primary department) and Computer Science. My research focuses on psychometrics, educational data mining and visual analytics.
Education
- University of Notre Dame
- Ph.D., Joint Degree in Quantitative Psychology and Computer Science (Expected 2025)
- M.S., Computer Science (Expected 2024)
- M.S., Applied and Computational Mathematics and Statistics (2023)
- University of Minnesota, Twin-Cities
- B.S., Computer Science & B.S., Psychology (2019)
Publications
- Lu, Y., Fowler, J., & Cheng, Y. (2025). A family of sequential item response models for multiple-choice, multiple-attempt test items. Psychometrika. Read more
- Lu, Y., & Wang, C. (2024). FAVis: Visual analytics of factor analysis for psychological research. In Proceedings of the 2024 IEEE Visualization Conference. Read more
- Lu, Y., Tong, L., & Cheng, Y. (2024). Advanced Knowledge Tracing: Incorporating Process Data and Curricula Information via an Attention-Based Framework for Accuracy and Interpretability. Journal of Educational Data Mining. Read more
- Ober, T. M., Lu, Y., Blacklock, C. B., Liu, C., & Cheng, Y. (2023). Development and validation of a cognitive load measure for general educational settings. Journal of Psychoeducational Assessment. Read more
- Lu, Y., Ober, T. M., Liu, C., & Cheng, Y. (2022). Application of Neighborhood Components Analysis to process and survey data to predict student learning of statistics. In Proceedings of the IEEE International Conference on Advanced Learning Technologies. Read more
Conference Presentations
- Lu, Y., & Cheng, Y. (2025). Detecting differential item functioning for multiple-attempt items. Paper presented at the National Council on Measurement in Education, Chicago, IL.
- Lu, Y., & Banjanovic, E. (2025). Maximizing assessment information after instruction: Using responses, time, and multiple attempts. Paper presented at the National Council on Measurement in Education, Chicago, IL.
- Maeda, H., & Lu, Y. (2025). Finding words associated with DIF: Predicting and describing differential item functioning using large language models. Paper presented at the National Council on Measurement in Education, Chicago, IL.
- Lu, Y., & Cheng, Y. (2024). Adaptive testing for multiple-choice, multiple-attempt test items. Paper presented at the National Council on Measurement in Education, Philadelphia, PA.
- Lu, Y., Tong, L., & Cheng, Y. (2023). Advanced knowledge tracing for intelligent tutoring systems: Incorporating process data and curricula information via an attention-based framework. Paper presented at the Trustworthy AI Lab for Education Summit, Notre Dame, IN.
- Lu, Y., & Cheng, Y. (2023). Bootstrap standard errors for LDA, NCA, and transformation-matrix methods with multiple solutions. Paper presented at the International Meeting of the Psychometric Society, College Park, MD.
- Lu, Y., & Cheng, Y. (2023). Extended sequential item response model for multiple-choice, multiple-attempt test items. Paper presented at the National Council on Measurement in Education, Chicago, IL.
- Ober, T. M., Denner, M., Lu, Y., Liu, C., & Cheng, Y. (2022). Comparing subjective and objective estimates of effort and performance on adaptive vs. non-adaptive computerized tests. Paper presented at the International Society of the Learning Sciences Annual Meeting (Virtual).
- Lu, Y., & Cheng, Y. (2022). Sequential item response model for multiple-choice, multiple-attempt test items. Paper presented at the National Council on Measurement in Education, San Diego, CA.
- Ober, T. M., Liu, C., Lu, Y., & Cheng, Y. (2022). Adaptive vs. non-adaptive test mode effects on effort, test anxiety, and performance. Paper presented at the National Council on Measurement in Education, San Diego, CA.
- Lu, Y., Weiss, D. J., & Wang, C. (2019). Multidimensional CAT measuring patient-reported outcomes in a hospitalized population. Paper presented at the International Association for Computerized Adaptive Testing, Minneapolis, MN.
Technical Skills
- Languages: Python, R, C++, JavaScript
- Frameworks: PyTorch, Vue.js, D3.js
- Tools: SPSS, PyMC, Armadillo, Qt
Contact
Feel free to reach out for collaborations or inquiries at ylu22@nd.edu.