Liuyi Jin, Machine Learning Engineer, Apple Seattle
Liuyi Jin, Machine Learning Engineer, Apple Seattle
Embedded Files
Office: Seattle
Email: liuyijin0601 - at - gmail.com
Bio
Bio
My name is Liuyi Jin. Since Oct 2026, I've been a machine learning engineer at Apple, working on LLM infra (e.g., distributed LLM inference).
My name is Liuyi Jin. Since Oct 2026, I've been a machine learning engineer at Apple, working on LLM infra (e.g., distributed LLM inference).
From Sept 2025 to Sept 2026, I was a Postdoc in the Department of Computer Science & Engineering at Washington University in St. Louis, working with Prof. Chenyang Lu on AI for Health. Prior to this, I obtained my Ph.D. degree in Computer Science from Texas A&M University, working with Prof. Radu Stoleru on applying AI/ML (e.g., LLM) to advance edge/cloud computing in emergency healthcare. Here are my CV and Google Scholar. If you believe my expertise aligns with your interests, feel free to reach out!
From Sept 2025 to Sept 2026, I was a Postdoc in the Department of Computer Science & Engineering at Washington University in St. Louis, working with Prof. Chenyang Lu on AI for Health. Prior to this, I obtained my Ph.D. degree in Computer Science from Texas A&M University, working with Prof. Radu Stoleru on applying AI/ML (e.g., LLM) to advance edge/cloud computing in emergency healthcare. Here are my CV and Google Scholar. If you believe my expertise aligns with your interests, feel free to reach out!
I'm also generally interested in building novel, practical, and secure networked computer systems to address a diverse range of cutting-edge problems across healthcare, human computer interaction, multimodal sensing, AR/VR, computer resource management, Earth Science, and more. I interned at XiaoMi, Facebook (now Meta), VMware, Baidu USA in the past summers.
I'm also generally interested in building novel, practical, and secure networked computer systems to address a diverse range of cutting-edge problems across healthcare, human computer interaction, multimodal sensing, AR/VR, computer resource management, Earth Science, and more. I interned at XiaoMi, Facebook (now Meta), VMware, Baidu USA in the past summers.
Education
Education
Texas A&M University, Sep 2018–Dec 2025
Ph.D., Computer Science, Advisor: Prof. Radu Stoleru
Dissertation: AI-enabled Next-generation Emergency Medical Services at the Edge
Texas A&M University, Sep 2016–May 2018
M.Sc., Petroleum Engineering, Advisor: Prof. John Lee
Thesis: Machine Learning Aided Production Data Analysis for Estimated Ultimate Recovery Forecasting
China University of Mining and Technology, Sep 2011–June 2015
B.E., Safety Engineering
Awards
Awards
BuildSys Best Paper Award, 2024
MobiSys Student Travel Grant, 2023
NanoCom Student Travel Grant, 2019
TAMU CSE Student Travel Grant, 2019, 2023
CUMT Outstanding Undergraduate, 2015
News
News
2025 Dec 17: I attended my graduation ceremony, and got my Ph.D. degree.
2025 Aug 29: I successfully defended my dissertation, and became Dr. Jin.
2023.05: EMSAssist MobiSys'23 full paper is awarded with all 4 artifact evaluation badges: available, evaluated functional, evaluated reusable, replicated.
2023.05: A poster is accepted to MobiSys'23: Towards Multi-Radio Access in 5G Networks. Congrats to Matan and Sangwoo for their first system publication in LENSS.
2023.02: I passed the Ph.D. Prelim Exam. I got the Ph.D. Candidacy.
Teaching and Mentor Experience
Teaching and Mentor Experience
Graduate Teaching Assistant:
Graduate Teaching Assistant:
TAMU CSCE 438/662 Distributed Processing Systems, Fall 2023, Spring 2024, Spring 2025
TAMU CSCE 483 Computer System Design (Senior Capstone Project), Fall 2024
TAMU CSCE 435/735 Parallel Computing, Summer 2024, Summer 2025
TAMU CSCE 120/121 Program Design and Concepts in C++, Summer 2023
Mentoring
Mentoring
PhD:
Pasan Dineth Gunawardena, TAMU CSE, applying AI/ML for health
Runzhi Wang, TAMU ECE, applying computer vision for mobile health
MS:
Sriram Suresh, TAMU CSE, profiling and optimizing GPU utilization for machine learning systems
Aaron Skouby, TAMU CSE, profiling and predicting battery consumption on mobile devices
Page updated
Google Sites
Report abuse