Fangzhou Wu

I'm a third-year Ph.D. student at University of Wisconsin–Madison, where I am fortunate to be co-advised by Kristin Eschenfelder and Sandeep Silwal. Prior to coming to Madison, I earned my bachelor's degree from Huazhong University of Science and Technology (HUST). I am currently a Student Researcher at Google DeepMind.

Email  /  Scholar  /  Twitter  /  Github

profile photo

Research

I am broadly interested in developing provably efficient algorithms to accelerate training and inference for foundation models and agents. My research aims to bridge theoretical insights and practical system design by integrating these algorithms into modern foundation-model-based applications.

Experience

Student Researcher, Google DeepMind
Research internship
May 2026

Selected Work

NEW DynMuon: A Dynamic Spectral Shaping View of Muon
Fangzhou Wu, Rikhav Shah, Sandeep Silwal, Qiuyi (Richard) Zhang
arXiv, 2026
code / arXiv
NEW Capturing LLM Capabilities via Evidence-Calibrated Query Clustering
Fangzhou Wu, Sandeep Silwal, Qiuyi (Richard) Zhang
arXiv, 2026
code / arXiv
Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective
Fangzhou Wu, Sandeep Silwal, Qiuyi (Richard) Zhang
ICLR, 2026
code / arXiv
Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
Fangzhou Wu, Sandeep Silwal
NeurIPS, 2025
code / arXiv
System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective
Fangzhou Wu, Ethan Cecchetti, Chaowei Xiao
arXiv, 2024
code / arXiv
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
Fangzhou Wu, Ning Zhang, Somesh Jha, Patrick McDaniel, Chaowei Xiao
arXiv, 2024
project page / arXiv

Talks & Presentations

Efficiency Across the Foundation Model Lifecycle: Training, Inference, and Evaluation Google DeepMind, May 2026
Efficient Capability-Aware LLM Systems: Capability Modeling, Routing, and Load Balancing UIUC, April 2026

Academic Service

Reviewer, ICLR 2024–2026
Reviewer, NeurIPS 2024–2026
Reviewer, ICML 2024, 2026

Teaching

TA, Applied Database Design (LIS 464), UW–Madison SP24–FA25

Template from here