Exploring the frontiers of machine learning and artificial intelligence through innovative research and practical applications.
Building and evaluating models that understand and generate across text, vision, and more.
Combining human feedback with automated AI learning to shape behavior and ensure reliability.
Developing adaptive ML methods to expand access and meet diverse needs effectively in practice.
Samintha Chandrasiri*, Neo Sud*, Tejas Polu, Emil Wilson, Brian Nguyen, David T. Lee
Submitted to IUI 2026 (under review)
🏆 Dean's Undergraduate Research Award
Jing Gu, Ashwin Nagarajan, Tejas Polu, Kaizhi Zheng, Ruijian Zha, Jie Yang, Xin Eric Wang
Second Workshop on Test-Time Adaptation: Putting Updates to the Test! at ICML 2025
Reza Habibi, Zhiyu Lin, Jiahong Li, Tejas Polu, Ashwin Nagarajan, Magy Seif El-Nasr
CHI 2025: Human-AI Interaction for Augmented Reasoning