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Jiachen Liu

PhD in CSE
University of Michigan, Ann Arbor

About Me 👋

Hello! I'm Amber (Jiachen) Liu, a fifth-year Ph.D. candidate in Computer Science at the University of Michigan (Umich), where I'm fortunate to work under the guidance of Prof. Mosharaf Chowdhury.

As an optimist and strong advocate of AGI, my research tries to build efficient systems that enable researchers to push the boundary of machine learning, particularly in Private Machine Learning and Large Language Models (LLMs). Currently, I'm exploring two exciting frontiers: enhancing the Quality-of-Experience (QoE) in LLM serving systems and developing systems for extremely large-scale LLM training.

To contribute to the research community, I maintain comprehensive paper collections on Private Machine Learning Systems and LLM Systems. I also serve as a Teaching Assistant for Systems for Generative AI at Michigan, where I've developed educational materials on LLM Systems Fundamentals, LLM Fine-tuning, and Vector Databases.

I'm always eager to engage in meaningful discussions about research and explore potential academic collaborations. Feel free to reach out via email or schedule a meeting to connect!

My academic journey has been enriched by wonderful collaborations with Prof. Sam Madden and Prof. Lei Cao at MIT, as well as Prof. Siqian Shen at Umich.

Publications 📃

User-Centric Machine Learning Systems

PhD Dissertation 2025

Jiachen Liu

EXP-Bench: Can AI Conduct AI Research Experiments?

Arxiv 2025

Patrick Tser Jern Kon*, Jiachen Liu*, Xinyi Zhu, Qiuyi Ding, Jingjia Peng, Jiarong Xing, Yibo Huang, Yiming Qiu, Jayanth Srinivasa, Myungjin Lee, Mosharaf Chowdhury, Matei Zaharia, Ang Chen (* Equal contribution).

The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization

Arxiv 2025

Jae-Won Chung, Jiachen Liu, Jeff Ma, Ruofan Wu, Oh Jun Kweon, Yuxuan Xia, Zhiyu Wu, Mosharaf Chowdhury

Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents

Arxiv 2025

Patrick Tser Jern Kon*, Jiachen Liu*, Qiuyi Ding, Yiming Qiu, Zhenning Yang, Yibo Huang, Jayanth Srinivasa, Myungjin Lee, Mosharaf Chowdhury, Ang Chen (* Equal contribution).

Evaluation Framework for AI Systems in "the Wild"

Arxiv 2025

Sarah Jabbour, Trenton Chang, Anindya Das Antar, Joseph Peper, Insu Jang, Jiachen Liu, Jae-Won Chung, Shiqi He, Michael Wellman, Bryan Goodman, Elizabeth Bondi-Kelly, Kevin Samy, Rada Mihalcea, Mosharaf Chowdhury, David Jurgens, Lu Wang

IaC-Eval: A code generation benchmark for Infrastructure-as-Code programs

NeurIPS 2024

Patrick Tser Jern Kon, Jiachen Liu, Yiming Qiu, Weijun Fan, Ting He, Lei Lin, Haoran Zhang, Owen M. Park, George Sajan Elengikal, Yuxin Kang, Ang Chen, Mosharaf Chowdhury, Myungjin Lee, Xinyu Wang

Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services

Arxiv 2024

Jiachen Liu, Zhiyu Wu, Jae-Won Chung, Fan Lai, Myungjin Lee, Mosharaf Chowdhury

Venn: Resource Management for Collaborative Learning Jobs

MLSys 2025

Jiachen Liu, Fan Lai, Ding Ding, Yiwen Zhang, Mosharaf Chowdhury.

Efficient Large Language Models: A Survey

TMLR 2024

Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, Mi Zhang.

FedTrans: Efficient Federated Learning via Multi-Model Transformation

MLSys 2024

Yuxuan Zhu, Jiachen Liu, Mosharaf Chowdhury, Fan Lai

Auxo: Efficient Federated Learning via Scalable Cohort Identification

SoCC 2023

Jiachen Liu, Fan Lai, Yinwei Dai, Aditya Akella, Harsha Madhyastha, Mosharaf Chowdhury.

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

ICML 2022

Fan Lai, Yinwei Dai, Sanjay S. Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury.

Fluid: Resource-Aware Hyperparameter Tuning Engine

MLSys 2021

Peifeng Yu*, Jiachen Liu*, Mosharaf Chowdhury (* Equal contribution).

Side Projects 👩🏻‍💻

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Curie

[Year of 2024] Curie is the first AI-agent framework designed for automated and rigorous scientific experimentation.

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ML.Energy Leaderboard

[Year of 2023] The goal of the ML.ENERGY Leaderboard is to give people a sense of how much energy LLMs would consume.

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FedScale

[Year of 2022] FedScale is a scalable and extensible open-source federated learning (FL) engine and benchmark.

Awards 🥇

  • Machine Learning and Systems Rising Stars 🌟 (Program of 2023)
  • Shanghai (China) Outstanding Graduate 🎓 (Top 5%) 2020

Experience 👩🏻‍💻

Company/Institution Position
Meta
Menlo Park, CA
Part-time Research Scientist, Llama Training Systems.
May 2024 - Dec 2024
University of Michigan
Ann Arbor, MI
Teaching Assistant, Systems for Generative AI (EECS 598)
Jan 2024 - May 2024
Apple
New York, NY
PhD Intern, Private Machine Learning Framework.
May 2022 - Aug 2022
MIT CSAIL Database group
Cambridge, MA
Research Intern, High-dimensional vector database.
May 2019 - Jan 2020
University of Michigan
Ann Arbor, MI
Teaching Assistant, Database Management (EECS484)
Sep 2019 - Aug 2020

Community Service 🎯

Organization Position
Umich CSE Graduate Students Organization
Ann Arbor, MI
DEI Chair
May 2023 - May 2024
SJTU-Joint Institute Student Union
SH, China
Vice President
June 2017 - June 2018

Education 🎓

Year School Degree
2020-2025 University of Michigan Master & Ph.D Candidate in Computer Science
2018-2020 University of Michigan B.S.E in Data Science
2016-2020 Shanghai Jiaotong University B.E in Electrical and Computer Engineering

Contact

刘嘉晨
amberljc
University of Michigan, Ann Arbor, 2260 Hayward St