I am a Ph.D. student in Electronic and Computer Engineering at
The Hong Kong University of Science and Technology,
advised by Prof. Yuan Xie.
I build systems for agentic AI infrastructure: runtimes, memory systems,
and serving stacks that make long-horizon LLM agents reliable, efficient, observable,
and deployable on real AI clusters.
My work connects systems research with deployable prototypes: agent workflow platforms,
trace and evaluation infrastructure, LLM/MoE serving, GPU/CPU-GPU runtime systems,
memory-centric AI accelerators, and hardware-software co-design for sparse and irregular
AI workloads. Before joining HKUST, I received my M.S. and B.S. in Microelectronics from
Fudan University.
I am open to collaboration on agent runtime systems, AI infrastructure, and efficient model
serving across the software-hardware boundary.
Research Interests
Agentic AI Infrastructure: runtime, tool use, state management, observability, and evaluation for long-horizon autonomous agents.
Agent Memory and Trace Systems: semantic locality, provenance, target construction, and replayable evaluation for coding agents and research agents.
LLM and MoE Serving: CPU-GPU cooperation, KV-cache movement, batching, speculative execution, and transfer-granularity optimization.
GPU-Cluster Systems: resource-efficient orchestration for heterogeneous AI clusters and production inference workloads.
Hardware-Software Co-Design: memory hierarchy, dataflow, sparse inference, and CIM/PIM acceleration for irregular AI computation.
08/2026 Open-sourced SpecDecode Viz and HKUST Student Expense Agent.
07/2026 Delta-MoE was accepted to ICCAD 2026.
06/2026 SwiftCIM was accepted to ESSERC 2026.
05/2026 STS was posted on arXiv.
04/2026 DS-CIM was accepted to DATE 2026.
11/2025 McPAL appeared in DAC 2025.
08/2025 DIRC-RAG appeared in ISLPED 2025.
07/2024 FullSparse was published at CF 2024.
Selected Projects
A few current projects and research prototypes around agent products, agent traces,
LLM serving, and efficient AI systems.
GrowthOS
AI-native growth consulting SaaS
GrowthOS's long-term goal is not to simply replace growth experts, but to turn their
experience into reusable system capabilities. In the early stage, it works like an
AI-native growth agency that validates workflows through real client projects. In the
middle stage, it becomes an agent-assisted SaaS platform that reduces repetitive expert
work in market research, competitor analysis, content drafting, and task tracking. In
the long run, it aims to make growth consulting, which is often expensive and available
only to a small number of companies, accessible as a basic service for more early-stage
companies.
A work-in-progress workspace for organizing coding-agent traces, target sampling,
input checks, and small vLLM/Slurm experiments.
AgentMem and AgentProf
Agent memory locality and workload profiling
Research prototypes for studying semantic locality in agent memory and profiling how
agent inference differs from ordinary chatbot serving in context growth, KV-cache
behavior, idle gaps, and batching.
HETC / LLM Serving Prototypes
Tensor compression and long-context serving experiments
Prototypes for efficient LLM systems, including modular tensor compression for fp16/bf16
tensors and speculation-guided ideas for sparse attention, KV prefetching, and long-context
serving.
Open Source Projects
Some recently open-sourced projects that make AI systems easier to inspect and everyday
research workflows easier to automate.
SpecDecode Viz
A real-time visualization playground for speculative decoding, showing draft tokens,
verification, accepted tokens, and the intuition behind serving speedups. Follow or
star the project if this helps you explain or debug speculative decoding.
If you are an HKUST student also struggling with tedious reimbursement forms, this
agent can automatically upload reimbursement materials and fill the required fields in
the HKUST finance system. You can use it as long as you have an LLM API key.
