Jason Wu
Jason Wu is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, Jason was a Research Scientist at Apple in the Human-Centered Machine Intelligence group, and he received a PhD in Human-Computer Interaction from Carnegie Mellon. In his research, Jason applies machine learning to optimize user interfaces for human-computer interaction. His research has resulted in over 30 publications in top venues for human-compute… ↓More
Joined department: Spring 2026
Research Areas
- Artificial Intelligence, Machine Learning, and Natural Language Processing
- Human-Computer Interaction
Education
B.S., Georgia Tech, Computer Science (2018)
M.S., Carnegie Mellon University, Human-Computer Interaction (2022)
Ph.D., Carnegie Mellon University, Human-Computer Interaction (2024)
Jason Wu is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, Jason was a Research Scientist at Apple in the Human-Centered Machine Intelligence group, and he received a PhD in Human-Computer Interaction from Carnegie Mellon. In his research, Jason applies machine learning to optimize user interfaces for human-computer interaction. His research has resulted in over 30 publications in top venues for human-computer interaction, user interface technology, accessibility, and machine learning, where he has received several best paper awards (CHI 2021, W4A 2021, IUI 2024) and honorable mention awards (CHI 2020, CHI 2023). His work has also been recognized outside of academic conferences by a Fast Company Innovation by Design Student Finalist Award, press coverage in major outlets such as TechCrunch and AppleInsider, and by the FCC Chair Awards for Advancements in Accessibility. Jason is a recipient of the NSF Graduate Research Fellowship and was selected as a Heidelberg Laureate Forum Young Researcher.
Selected Publications
Wu, J., Peng, Y.-H., Li, A. X. Y., Swearngin, A., Bigham, J. P., & Nichols, J. (2024). UIClip: A data-driven model for assessing user interface design. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST ’24) (16 pp.). Association for Computing Machinery. https://doi.org/10.1145/365477...
Wu, J., Schoop, E., Leung, A., Barik, T., Bigham, J. P., & Nichols, J. (2024). UICoder: Finetuning large language models to generate user interface code through automated feedback. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 7511–7525). Association for Computational Linguistics.
Wu, J., Wang, S., Shen, S., Peng, Y.-H., Nichols, J., & Bigham, J. P. (2023). WebUI: A dataset for enhancing visual UI understanding with web semantics. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23) (Article 286, pp. 1–14). Association for Computing Machinery. https://doi.org/10.1145/354454...
Wu, J., Todi, K., Chan, J., Myers, B. A., & Lafreniere, B. (2024). FrameKit: A tool for authoring adaptive UIs using keyframes. In Proceedings of the 29th ACM International Conference on Intelligent User Interfaces (IUI ’24) (pp. 660–674). Association for Computing Machinery. https://doi.org/10.1145/364054...
Zhang, X., de Greef, L., Swearngin, A., White, S., Murray, K., Yu, L., Shan, Q., Nichols, J., Wu, J., Fleizach, C., Everitt, A., & Bigham, J. P. (2021). Screen recognition: Creating accessibility metadata for mobile applications from pixels. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21) (Article 275, pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/341176...