Biography
Yueying Li is a PhD student studying the intersection of computer system and machine learning, advised by Prof. Christina Delimitrou and Edward Suh on problems spanning efficient and reliable cloud system. More broadly, I’m interested in system for machine learning and machine learning for system. Specifically, I focus on scheduling policies and mechanisms across different stacks. Last summer I interned at Microsoft Research on automated anomaly detection and root cause analysis for cloud services, and a summer before that, I did research on designing new scheduling abstractions for microservice applications with Intel Labs. I am also a keen observer on recent ML and RL papers, and I also have the fortune to work with Yuandong Tian on efficient planning offline RL; Robbert Van Renesse and Lorenzo on distributed shared log.
- Artificial Intelligence
- Computer System, Computer Architecture
- Reinforcement Learning
Visiting PhD in CSAIL, 2023
Massachusetts Institute of Technology
PhD in Computer Science, 2020
Cornell University
BSc in Computer Engineering, Minor in Math (Highest Honors), 2017
University of Michigan
BSc in Electrical and Computer Engineering, 2015
Shanghai Jiao Tong University
Recent News
[5/3/24] [Paper] Presenting LibPreemptible in HPCA in Session 7C on Tuesday. Thank you for all the collaborators, mentors and organizers. Looking forward to catching up with friends and meeting new people in the community!
[23/03/23] [Award] Ditto Selected as IEEE Micro Top Pick!
[10/1/24] [Presentation] Talks at Tsinghua IIIS, UCF, Shanghai Jiao Tong University. Thank you for the inspiring discussions and the warm welcome!
[23/10/23] [Mentorship] Our PRIMES mentees finished their project on using RL for serverless resource management. Congrats to Alan and Even! Report
[3/10/23] [Poster] Presented my earlier work in ACE center meeting at UIUC. Thank you for the initiative and for the inspiring discussions with the faculty and students in UIUC!
[29/09/23] [Poster] Presented a poster on MIT Cloud and AI workshop on automating configuration search for data ingestion pipelines.
[23/09/23] [Grant] Our group received an ACCESS Discovery Allocation Award from the NSF in advanced computing infrastructure to support my proposal titled “Democratizing Reliable Large Language Model Serving Systems”. Thank you, NSF!
Selected Awards
Departmental Fellowship (Cornell University), 2020
Outstanding Graduate of Shanghai, 2019
GSFEI Top Scholar Recruitment Award (University of Washington), 2019
University Honors, Dean’s List (All semesters) (University of Michigan)
Marian Sarah Parker Prize Nominee, EECS Dept of UM, 2019
Meritorious Winner, Mathematical Contest in Modeling, 2018
Tang Junyuan Scholarship (2/279)
National Scholarship (2%), 2017
Intern Experience
Responsibilities include:
- Cloud efficiency group
- Machine learning modeling and system deployment
Responsibilities include:
- System and software research group - performance optimization and tracing
- New architecture feature exploration
Responsibilities include:
- Architecture exploration with new ISA simulator
- Working on apple matrix engine and memory compression engine
Services
Artifact Evaluation Committee, MLSys 2023, ASPLOS 2022
Reviewer, Machine Learning Journal - Reinforcement Learning for Real Life
ISCA Undergrad Architecture Mentoring (uArch) Workshop Mentor, 2022
CALM Founding Committee, Computer Architecture Student Association (CASA) Steering Committee
Women in Systems Podcast and Student at Systems, Founding Members
Cornell CS PhD Admission Student Reviewer, 2022
NCWIT Aspirations in Computing Workshop at Women@Apple, Panel Leader, 2020
IEEE Student Branch at U of Michigan, Board Member and Department Relation Chair, 2018
Michigan China Forum 2018 Mobility Panel, Panel Chair, 2018