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.
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Experience
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Student Researcher, Google DeepMind
Research internship
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May 2026 |
Selected Work
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NEW
DynMuon: A Dynamic Spectral Shaping View of Muon
Fangzhou Wu,
Rikhav Shah,
Sandeep Silwal,
Qiuyi (Richard) Zhang
arXiv, 2026
code
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arXiv
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NEW
Capturing LLM Capabilities via Evidence-Calibrated Query Clustering
Fangzhou Wu,
Sandeep Silwal,
Qiuyi (Richard) Zhang
arXiv, 2026
code
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arXiv
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Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective
Fangzhou Wu,
Sandeep Silwal,
Qiuyi (Richard) Zhang
ICLR, 2026
code
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arXiv
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Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
Fangzhou Wu,
Sandeep Silwal
NeurIPS, 2025
code
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arXiv
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System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective
Fangzhou Wu,
Ethan Cecchetti,
Chaowei Xiao
arXiv, 2024
code
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arXiv
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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
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arXiv
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Academic Service
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| Reviewer, ICLR |
2024–2026 |
| Reviewer, NeurIPS |
2024–2026 |
| Reviewer, ICML |
2024, 2026 |
Teaching
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TA, Applied Database Design (LIS 464), UW–Madison
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SP24–FA25
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