Hi, I am Banruo.
劉般若
I work on AI systems, LLM serving, and distributed infrastructure. I like building practical systems that make machine learning faster, more reliable, and easier to use.
About
I am a Ph.D. student in Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Fan Lai, and I expect to receive my master's degree this year while continuing my Ph.D. I received my B.Eng. in Computer Science from Tsinghua University. I am interested in the systems side of AI: serving, scheduling, GPU clusters, cloud infrastructure, and the engineering details that turn research ideas into working platforms. Before UIUC, I was a research assistant at SANDS Lab, KAUST, advised by Prof. Marco Canini, and at SysLab, University of Washington, advised by Prof. Arvind Krishnamurthy and Prof. Ratul Mahajan.
Experience
Microsoft
Currently working on system and harness for coding agent.
Worked on AI systems research related to real-time agentic serving.
Publications
Real-Time Agentic Serving via Just-in-Time Dataflow Execution
In submission. Details limited while under review.
Systems work for real-time agentic LLM serving.
Single-agent or Multi-agent Systems? Why Not Both?
In submission. Mingyan Gao*, Yanzi Li*, Banruo Liu*, Yifan Yu, Phillip Wang, Ching-Yu Lin, Fan Lai.
Empirical study of single-agent and multi-agent LLM systems; proposes routing and cascade strategies for cost-quality tradeoffs in agentic applications.
Compass: SLO-aware Query Planner for Compound AI Serving at Scale
VLDB 2026. Banruo Liu, Wei-Yu Lin, Minghao Fang, Yihan Jiang, Fan Lai.
SLO-aware query planner for compound AI pipelines; improves service goodput by 2.4-5.1x and reduces deployment cost by 3.8-4.5x.
JITServe: SLO-aware LLM Serving with Imprecise Request Information
NSDI 2026. Wei Zhang*, Zhiyu Wu*, Yi Mu, Rui Ning, Banruo Liu, Nikhil Sarda, Myungjin Lee, Fan Lai.
LLM serving scheduler for latency-sensitive, deadline-sensitive, and compound requests under response-length and dependency uncertainty.
High-level Programming for Application Networks
NSDI 2025. Xiangfeng Zhu, Yuyao Wang, Banruo Liu, Yongtong Wu, Nikola Bojanic, Jingrong Chen, Gilbert Bernstein, Arvind Krishnamurthy, Sam Kumar, Ratul Mahajan, Danyang Zhuo.
High-level language, compiler, and controller for programmable application networks in microservice systems.
Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation
APSys 2024. Banruo Liu, Mubarak Adetunji Ojewale, Yuhan Ding, Marco Canini.
Distributed DNN training emulator that executes real training nodes while emulating networked collective communication.
Application Defined Networks
HotNets 2023. Xiangfeng Zhu, Weixin Deng, Banruo Liu, Jingrong Chen, Yongji Wu, Thomas Anderson, Arvind Krishnamurthy, Ratul Mahajan, Danyang Zhuo.
Position paper on application-defined networking for programmable microservice communication and application-aware network functions.
* denotes equal contribution where applicable.