Charlie Ruan

Charlie Ruan


Carnegie Mellon University


I am a first-year MSCS student at Carnegie Mellon University, where I am fortunate to work with Prof. Tianqi Chen as part of the Catalyst Group.

My focus is at the intersection of machine learning and systems, with various open-source development experience. I am the current lead of Web LLM, a core contributor to MLC LLM, and have been contributing to ApacheTVM. I also contributed to TensorFlow as part of Google’s TF Runtime team in Summer 2023.

Prior to coming to CMU, I obtained my B.S. degree in Computer Science and Operations Research from Cornell University, where I was fortunate to work with Prof. Christopher De Sa on distributed ML and with Prof. Jim Dai on reinforcement learning.

  • Machine Learning Systems
  • Distributed Systems
  • MS in Computer Science, 2025

    Carnegie Mellon University

  • BS in Computer Science & Operations Research, 2023

    Cornell University


(2023). Coordinating Distributed Example Orders for Provably Accelerated Training. In NeurIPS'23.

PDF Cite Code Poster


Core Contributor
June 2023 – Present Pittsburgh, PA
Enable universal native deployment for LLMs through machine learning compilation techniques
Project Lead
June 2023 – Present Pittsburgh, PA
Leading the project to bring LLMs to run locally in client-side browser with WebGPU acceleration

Research Experience

Prof. Tianqi Chen & Prof. Zhihao Jia, Carnegie Mellon University
Research Assistant
Prof. Tianqi Chen & Prof. Zhihao Jia, Carnegie Mellon University
March 2024 – Present Pittsburgh, PA
Investigating distributed LLM serving systems
Prof. Christopher De Sa, Cornell University
Research Assistant
Prof. Christopher De Sa, Cornell University
September 2022 – June 2023 Ithaca, NY
Investigated finding provably better data permutations in distributed learning. CD-GraB was accepted by NeurIPS'23
Prof. Jim Dai, Cornell University
Research Assistant
Prof. Jim Dai, Cornell University
November 2021 – September 2022 Pittsburgh, PA
Investigated using variance-reduction method approximating martingale-process in reinforcement learning with large state space

Industry Experience

Software Engineer Intern
June 2023 – August 2023 Sunnyvale, CA
Worked on Core ML’s Distributed Runtime team, optimizing TensorFlow’s checkpoint to reduce wasted TPU cycles
Google Cloud
Software Engineer Intern
August 2022 – October 2022 Sunnyvale, CA
Worked on Technical Infrastructure’s Platform team, deploying accelerators in Google data centers using OpenBMC, implementing Linux daemon and firmware update APIs
Amazon Robotics
Software Engineer Intern
May 2022 – July 2022 Greater Boston, MA
Worked on Robotic Storage Technologies team, improving worker’s interaction with autonomous warehouse robots
XPENG Motors
Software Engineer Intern
June 2021 – August 2021 Shanghai, China
Optimized sensor fusion algorithms for XPeng’s self-driving cars
Morgina Information Technology
Software Engineer Intern
June 2020 – July 2020 Shanghai, China
Optimized multi-object tracking algorithm with millimeter-wave radar


I will be applying for Fall 2025 PhD programs in Computer Science. Please feel free to contact me!