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.
We're thrilled to announce the acceptance of our SIGCSE 2026 research papers. Join us at SIGCSE 2026 TS to explore how our findings can be leveraged into curricula to improve student learning outcomes.
Pacing for Mastery: Optimizing LLM Interactions for Learning
Karena Tran, Ge Gao, Angela Lombard, Tyler Yu, Haoning Jiang and Thomas Yeh
Fighting Fire with Fire: LLM-Assisted Grading of Handwritten CS Assessments
Jared Apillanes, Jason Weber, Sergio Gago-Masague, Jennifer Wong-Ma and Thomas Yeh
We're thrilled to announce the acceptance of our SIGCSE 2025 research paper exploring the transformative potential of Large Language Models (LLMs) in computer science education. 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. Join us at SIGCSE 2025 TS to explore how our findings can be leveraged into curricula, empowering the next generation of programmers.
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