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Yangfan Zhou

25 papers hereh-index 242.2k citations95 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author19
  • last author6

Across the 25 of 25 papers where every author was matched, so the position is known.

fields
  • cs.SE13
  • cs.CR3
  • cs.DC3
  • cs.LG2
  • cs.PL2
  • cs.CY1
same name
  • Yangfan Zhou — 3 papers, h 0
  • Yangfan Zhou — 2 papers, h 5
  • Yangfan Zhou — 2 papers, h 3
  • Yangfan Zhou — 2 papers, h 2
  • Yangfan Zhou — 2 papers, h 5
  • Yangfan Zhou — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedDeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

21 citations · 105 across the 25 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.DC2026

SmartGen: Seamless Disaggregated LLM Inference with Selective KV Cache Transfer

Xuchuan Luo, Jiacheng Shen, Xin Wang +1

Disaggregating the prefill and decoding stages of large language model (LLM) inference into two separate sets of nodes is widely adopted in today's LLM serving systems. However, su…

cs.SE2026

Bringing Managed Language Support to WebAssembly with External Library Linking

Shuyao Jiang, Ruiying Zeng, Yangfan Zhou +1

WebAssembly (Wasm) has emerged as a powerful bytecode format for running applications with near-native performance in portable and secure environments. However, while Wasm currentl…

cs.SE2026

Debugging Performance Issues in WebAssembly Runtimes via Mutation-based Inference

Ruiying Zeng, Shuyao Jiang, Wenxuan Zhao +1

Performance debugging in WebAssembly (Wasm) runtimes is essential for ensuring the robustness of Wasm, especially since performance issues have frequently occurred in Wasm runtimes…

cs.OS2026

EdgeFlow: Fast Cold Starts for LLMs on Mobile Devices

Yongsheng Yan, Jiacheng Shen, Xuchuan Luo +1

Deploying large language models (LLMs) on mobile devices is an emerging trend to enable data privacy and offline accessibility of LLM applications. Modern mobile neural processing…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.