A list of my research papers can be seen on my Google Scholar page.
Be Like Water: Adaptive Floating Point for Machine Learning
Thomas Yeh, Max Sterner, Zerlina Lai, Brandon Chuang and Alexander Ihler
Proceedings of the 39th International Conference on Machine Learning, 2022.
Our study presents a novel taxonomy to evaluate cognitive offloading of student-AI interactions.
TBA
In Proceedings of the 2nd ACM Virtual Global Computing Education Conference (SIGCSE Virtual 2026).
Our study presents statistical evidence on students' immediate learning and knowledge retention when using commercial vs pedagogical AI tutors.
Pacing for Mastery: Optimizing LLM Interactions for Learning
Karena Tran, Ge Gao, Angela Lombard, Tyler Yu, Haoning Jiang and Thomas Yeh
In Proceedings of the 57th ACM Technical Symposium on Computer Science
Education V. 1 (SIGCSE TS 2026).
Our study compares AI-assisted to manual grading of handwritten CS assessments.
Fighting Fire with Fire: LLM-Assisted Grading of Handwritten CS Assessments
Jared Apillanes, Jason Weber, Sergio Gago-Masague, Jennifer Wong-Ma and Thomas Yeh
In Proceedings of the 57th ACM Technical Symposium on Computer Science
Education V. 1 (SIGCSE TS 2026).
As LLMs become integral tools for experienced programmers, their impact on novice learners remains a critical question. Our study delves into this challenge, revealing how interactive LLMs can enhance code generation accuracy for beginners and improve their prompting skills. Our approach not only boosts learning outcomes but also addresses equity concerns in CS education.
Bridging Novice Programmers and LLMs with Interactivity
Thomas Y. Yeh, Karena Tran, Ge Gao, Tyler Yu, Wai On Fong, and Tzu-Yi Chen.
2025. Bridging Novice Programmers and LLMs with Interactivity.
In Proceedings of the 56th ACM Technical Symposium on Computer Science
Education V. 1 (SIGCSE TS 2025). ACM, New York, NY, USA, 7 pages.
https://doi.org/10.1145/3641554.3701867