Tianyi Xu

I’m Tianyi Xu, a senior at the University of Wisconsin–Madison (B.S. in Computer Science, Data Science, and Mathematics; expected Dec 2025). I am advised by Prof. Junjie Hu and Prof. Claudia Solís-Lemus at UW–Madison. I work closely with Shaobo Wang and Prof. Linfeng Zhang at Shanghai Jiao Tong University. I’ve previously collaborated with Prof. Pedro Morgado and Prof. Chaowei Xiao.

I’m interested in building modern AI systems that are efficient, generalizable, and capable of understanding and acting across multiple modalities, especially under limited supervision and real-world constraints. Concretely, my work focuses on:

1. Data-centric & label-efficient learning — Designing methods for data selection, mixing, and self/weak supervision so that large-scale models can learn from noisy, heterogeneous data instead of only clean benchmarks.

2. Foundation models & agents— Building and steering pretrained models (LLMs, VLMs, etc.) for specific tasks, with an emphasis on efficiency, reliability, and agentic behaviors such as planning, reasoning, and safe decision-making.

3. Multimodal learning — Representation learning and building systems that perceive, act, and reason across different modalities.

4. AI for science and society — Applying these ideas to real-world problems where data is scarce or noisy: biodiversity monitoring from soundscapes, tone-aware speech modeling for accessibility, and multimodal toolkits/benchmarks for biomedical and clinical AI.

I’m seeking Ph.D. opportunities for Fall 2026.