# Chijun Sima — Full Context

> This file provides comprehensive information about Chijun Sima for LLM agents and AI systems. For a concise summary, see /llms.txt.

## Snapshot

- Last updated: 2026-10-02
- Website: https://www.chijunsima.com/
- Canonical description: Chijun Sima — CS PhD student at UT Austin (ML for systems, systems for ML). Co-first author of Ekko (OSDI '22). LLVM developer with commit access.

## Machine-readable endpoints

- [llms.txt](https://www.chijunsima.com/llms.txt): Concise LLM-oriented summary of the site owner and work. (text/plain)
- [llms-full.txt](https://www.chijunsima.com/llms-full.txt): Expanded context for deeper agent or retrieval workflows. (text/plain)
- [profile.json](https://www.chijunsima.com/profile.json): Machine-readable profile, experience, contact, and links. (application/json)
- [publications.json](https://www.chijunsima.com/publications.json): Selected publications in a clean JSON structure. (application/json)
- [feed.json](https://www.chijunsima.com/feed.json): JSON Feed of publications and career milestones. (application/feed+json)
- [openapi.json](https://www.chijunsima.com/openapi.json): OpenAPI description for the public read-only profile endpoints. (application/json)
- [agent skills](https://www.chijunsima.com/.well-known/agent-skills/index.json): Agent Skills discovery index for using the site profile endpoints. (application/json)

## Identity

- Full name: Chijun Sima
- Current role: Computer Science PhD student
- Affiliation: The University of Texas at Austin
- Department: Department of Computer Science
- Location: Austin, Texas, USA
- Research interests: Machine learning for systems; Systems for machine learning

## Advisors

- Chenfeng Xu, Assistant Professor, Department of Computer Science, The University of Texas at Austin: https://www.chenfengx.com
- Aditya Akella, Professor & Regents Chair, Department of Computer Science, The University of Texas at Austin: https://www.cs.utexas.edu/~akella/

## Contact

- Email: simachijun@gmail.com
- Website: https://www.chijunsima.com/
- Google Scholar: https://scholar.google.com/citations?user=8-HD_IEAAAAJ&hl=en
- LinkedIn: https://www.linkedin.com/in/chijun-sima/

## Education

- PhD in Computer Science, The University of Texas at Austin, Aug 2026 – Present
  - Department of Computer Science.
- B.Eng. in Computer Science and Technology (Innovation Class), South China University of Technology, Sep 2016 – Jun 2020
  - GPA 3.85 / 4.00 · Rank 1 / 28

## Selected publications

### 1. Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update

- Venue: 16th USENIX Symposium on Operating Systems Design and Implementation, 2022
- Authors: Chijun Sima*, Yao Fu*, Man-Kit Sit, Liyi Guo, Xuri Gong, Feng Lin, Junyu Wu, Yongsheng Li, Haidong Rong, Pierre-Louis Aublin, Luo Mai
- URL: https://www.usenix.org/conference/osdi22/presentation/sima
- Note: * co-first author. Supervised by Luo Mai.
- Summary: Low-latency model update system for multi-terabyte deep learning recommendation models, achieving 2.4s update latency, 10,000x model-size scaling, and large production impact in WeChat.

## Industry and open-source experience

### The University of Texas at Austin — PhD Student in Computer Science
- Period: Aug 2026 – Present
- Location: Austin, Texas, USA
  - Advised by Chenfeng Xu and Aditya Akella.

### Tencent's WeChat division — Senior Software Development Engineer
- Period: Jul 2020 – Aug 2026
- Location: Guangzhou, China
- Project: Ekko: low-latency model update for multi-terabyte DLRMs (published in part as OSDI '22)
  - Summary: 2.4 s model-update latency and 10,000× model-size scaling for multi-terabyte recommendation models; serves 1 B+ users daily in WeChat.
  - Problem. Scaling DLRMs improved offline accuracy but degraded online engagement; root cause: stale models from increased model-update latency.
  - Key idea. Co-designed deployment mechanisms with model-aware policies (compressed update dissemination, accuracy-aware scheduling, SLO-aware placement, safe rollback).
  - Technical contributions. WAN bandwidth −92 %, machine cost −49 %, 2.4 s model-update latency; 10,000× model-size scaling (GB → tens of TB).
  - Outcomes. Core techniques published as OSDI '22 (co-first author). Deployed in WeChat recommendation stacks, serves 1 B+ users daily. Official WeChat blog reports +40 % DAU and +87 % total VV over six months after full adoption (alongside product iteration and operations).
- Project: Data and feature platform: safe, scalable pipelines
  - Summary: WebAssembly-based runtime with in-process isolation; data movement reduced up to 1,200× on representative workloads.
  - Problem. Modern feature pipelines are long and increasingly multimodal; cross-process operator composition creates high overhead and expensive data movement.
  - Approach. WebAssembly-based runtime for in-process isolation (safety + resource constraints) and locality-aware operator placement near data sources.
  - Outcome. Data movement reduced up to 1,200× on representative workloads; widely used within WeChat for data preparation.

### LLVM — Developer (commit access)
- Period: 2018 – Present
- Focus: Google Summer of Code 2018
  - Improved Semi-NCA performance and optimization pipeline; shipped in LLVM 9.0 (reported speedups up to 1,980× on real-world samples).
  - Unified APIs on dominator trees; shipped in LLVM 7.0.

## Academic service

- Reviewer: NeurIPS 2026
- Reviewer: BMVC 2026
- Reviewer: CVPR 2025

## Awards

- Tencent Technology Breakthrough Award (Gold Prize) — Project Lead, Ekko (internal highest technical honor) (2022)
- Bronze Medal, ACM-ICPC Asia Xi'an Regional Contest (2017)
- Second Prize, 15th China Collegiate Programming Contest (Guangdong Division, out of 177 teams)

## Talks

- "Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update"
  - Tencent's WeChat division, Shenzhen (Jun 2022)
  - DataFun, Virtual (Aug 2022)
  - TechBeat, Virtual (Sep 2022)

## Selected company and press write-ups about Ekko

- OSDI 2022 paper: https://www.usenix.org/conference/osdi22/presentation/sima
- WeChat official write-up: https://mp.weixin.qq.com/s/gBD3mdoRRlGI8bmXp2OBMA
- Tencent official write-up: https://mp.weixin.qq.com/s/hS5ZebOC7oQz_Itud0A_Rg
- Synced Review / JIQIZHIXIN: https://mp.weixin.qq.com/s/Vriupgqusj1zJmSuYU9WjA
- Google Scholar: https://scholar.google.com/citations?user=8-HD_IEAAAAJ&hl=en

## Keywords

Machine Learning for Systems, Systems for Machine Learning, WebAssembly, LLVM, Compilers, Distributed Systems