Yejia Liu

Yejia Liu

PhD Student

Biography

I am currently pursuing my PhD at UC Riverside advised by Prof. Shaolei Ren. My research interests are in Responsible AI, Continual Learning and Efficiency on Edge Intelligence. Previously, I held positions as a Cloud Software Engineer at Amazon and Tencent. Prior to that, I completed my Master’s degree at Simon Fraser University, where I was supervised by Prof. Oliver Schulte and Prof. Jiannan Wang. My undergraduate studies were at South China University of Technology.

During my leisure time, I enjoy traveling, playing the GuZheng, and savoring tasty food 😊.

Interests
  • Machine Learning
  • Data Science
  • GuZheng
Education

  • Ph.D. in Computer Science, 2021 - Present

    UC Riverside

  • M.S. in Computer Science, 2016 - 2018

    Simon Fraser University

  • B.Eng. in Software Engineering, 2012 - 2016

    South China University of Technology

Selected Publications

Quickly discover relevant content by filtering publications.
Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren (2024). Building Socially-Equitable Public Models. In ICML 2024.

PDF Code

Yejia Liu, Pengfei Li, Daniel Wong, Shaolei Ren (2024). Geographical Server Relocation: Opportunities and Challenges. In HotCarbon 2024 (co-located with OSDI).

PDF HotCarbon

Pengfei Li, Yejia Liu, Jianyi Yang, Shaolei Ren (2024). Towards Socially and Environmentally Responsible AI. In HotEthics 2024 (co-located with ASPLOS).

PDF HotEthics2024

Yejia Liu, Vidit Jain, Pengfei Li, Yinglun Zhu, Shaolei Ren (2024). LLM-Assisted Charging Scheduling for Decarbonizing Electrical Vehicle. In Submission.

Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren (2024). Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces. In TinyML 2024.

PDF TinyML

Yejia Liu, Wang Zhu, Shaolei Ren (2022). Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning. In NeurIPS 2022.

PDF

Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren (2022). MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption. In TinyML 2023.

PDF TinyML

Yejia Liu, Weiyuan Wu, Lampros Flokas, Jiannan Wang, Eugene Wu (2022). Enabling SQL-based Training Data Debugging for Federated Learning. In VLDB 2022.

PDF Code

Lampros Flokas, Weiyuan Wu, Yejia Liu, Jiannan Wang, Eugene Wu (2022). Complaint-Driven Training Data Debugging at Interactive Speeds. In SIGMOD 2022.

PDF

Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu (2022). LeHDC: Learning-Based Hyperdimensional Computing Classifier. In DAC 2022.

PDF

Yejia Liu, Oliver Schulte, Chao Li (2018). Model trees for identifying exceptional players in the NHL and NBA drafts. In MLSA@ECML-PKDD 2018.

PDF

Industry Experience

 
 
 
 
 
Data Scientist Intern
Microsoft
Jun 2024 – Sep 2024 Redmond, WA, the USA
 
 
 
 
 
Data Scientist Intern
Microsoft
Jun 2023 – Sep 2023 Redmond, WA, the USA
 
 
 
 
 
Software Engineer (Full-Time)
Tencent Cloud Infra
Aug 2019 – Apr 2020 ShenZhen, China
 
 
 
 
 
Software Engineer (Full-Time)
Amazon Web Services, Amazon
Sep 2018 – Jun 2019 Vancouver, Canada
 
 
 
 
 
Software Engineer Intern
Amazon Web Services
May 2017 – Aug 2017 Vancouver, Canada

Contact