Qinyu Xu

Ph.D. Student
Courant Institute of Mathematical Sciences
New York University

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Qinyu Xu

About

I am a Ph.D. student in Computer Science at the Courant Institute of Mathematical Sciences, New York University, co-advised by Anirudh Sivaraman and Jinkun Geng. Before NYU, I received my B.E. in Computer Science and Engineering from Tsinghua University. I work on distributed systems and formal verification, and how both can make machine learning systems more reliable.

Research Interests

  • Distributed systems
  • Formal verification
  • Machine learning systems

Research

vCheck: Verifiable Checkpointing for LLM Training
New York University, 2025 – Present. Advisors: Anirudh Sivaraman, Jinkun Geng.

  • Building the first formally verified checkpointing system for LLM training, designed so that training can checkpoint frequently without slowing down, while proving that every recovered checkpoint is consistent.
  • Designed a decoupled architecture that makes the system both fast and provable, with a proof that stays valid as the surrounding PyTorch stack evolves.
  • Built the first RDMA library verified in separation logic, reusable by other verified systems.

Tactic: Sparse Attention for Long-Context LLM Inference
University of Washington, 2024 – 2025. Supervisor: Baris Kasikci.

  • Proposed and developed a dynamic token-selection method for KV cache optimization in long-context LLM inference.
  • Designed a three-phase approach based on spherical k-means clustering, token ordering, and attention-score distribution modeling.
  • Developed a custom kernel to accelerate k-means clustering.

NanoFlow: High-Throughput LLM Serving
University of Washington, 2024 – 2025. Supervisor: Baris Kasikci.

  • Characterized GPU utilization and operator-level performance for the serving pipeline.

Open-Structure Table Extraction
Microsoft Research Asia, 2023 – 2024. Supervisor: Haoyu Dong.

  • Built a data pipeline transforming large open-domain corpora into high-quality annotated tables.
  • Designed an evaluation framework for benchmarking models on open-structure table detection.

Education

New York University — New York, NY
Ph.D. in Computer Science, 2025 – Present

Tsinghua University — Beijing, China
B.E. in Computer Science and Engineering, 2021 – 2025

Publications and Posters

vCheck: Building Efficient and Verifiable Checkpointing for LLM Training
Qinyu Xu, Jinkun Geng, Joseph Tassarotti, Anirudh Sivaraman
SOSP 2026 Poster, Prague, Czech Republic
Poster List

Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
Kan Zhu, Tian Tang, Qinyu Xu, Zhan Jin, Yile Gu, Zhichen Zeng, Rohan Kadekodi, Liangyu Zhao, Ang Li, Arvind Krishnamurthy, Baris Kasikci
ICLR 2026, Rio de Janeiro, Brazil
Paper

NanoFlow: Towards Optimal Large Language Model Serving Throughput
Kan Zhu, Yufei Gao, Yilong Zhao, Liangyu Zhao, Gefei Zuo, Yile Gu, Dedong Xie, Tian Tang, Qinyu Xu, Zihao Ye, Keisuke Kamahori, Chien-Yu Lin, Ziren Wang, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci
OSDI 2025, Boston, MA, USA
Paper

OpenTE: Open-Structure Table Extraction from Text
Haoyu Dong, Mengkang Hu, Qinyu Xu, Haochen Wang, Yue Hu
ICASSP 2024, Seoul, Korea
Paper