I am a Data Scientist at Microsoft in Redmond, WA. I completed my Ph.D. in Computer Science at the University of California, Riverside in 2025, advised by Prof. Shaolei Ren. My research focuses on building efficient, equitable, and responsible systems across database and machine learning.
Earlier, I worked as a Software Engineer at Amazon and Tencent, and interned at Microsoft Research. I earned my Master’s in Computing Science at Simon Fraser University, supervised by Prof. Oliver Schulte and Prof. Jiannan Wang, and my Bachelor’s in Software Engineering at South China University of Technology.
In my leisure time, I enjoy traveling, playing the GuZheng, and savoring tasty food.
News
- Nov 2025 New preprint: Predicting Public Health Impacts of Electricity Usage, accepted to NeurIPS 2025 ResponsibleFM Workshop.
- Aug 2025 Joined Microsoft as a Data Scientist in Redmond, WA.
- Jul 2025 Got my Ph.D. in Computer Science at UC Riverside!
- Feb 2025 Vector Optimization on Low-Dimensional VSA accepted to CPAL 2025.
- Jul 2024 Geographical Server Relocation accepted to HotCarbon 2024.
- May 2024 Building Socially-Equitable Public Models accepted to ICML 2024.
- Apr 2024 Towards Socially and Environmentally Responsible AI accepted to HotEthics @ ASPLOS 2024.
- Mar 2024 Scheduled Knowledge Acquisition on Lightweight VSAs accepted to TinyML 2024.
Selected Publications
- Wang Bill Zhu*, Miaosen Chai*, Shangshang Wang, Yejia Liu, Song Bian, Honghua Dong, Willie Neiswanger, Robin Jia. Precise Debugging Benchmark: Is Your Model Debugging or Regenerating? Annual Conference of the Association for Computational Linguistics (ACL), 2026.
- Yejia Liu, Zhifeng Wu, Pengfei Li, Shaolei Ren. Predicting Public Health Impacts of Electricity Usage. Socially Responsible and Trustworthy Foundation Models Workshop at NeurIPS (ResponsibleFM, NeurIPS), 2025.
- Shijin Duan, Yejia Liu, Gaowen Liu, Ramana Rao Kompella, Shaolei Ren, Xiaolin Xu. Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture. Conference on Parsimony and Learning (CPAL), 2025.
- Yejia Liu, Pengfei Li, Daniel Wong, Shaolei Ren. Geographical Server Relocation: Opportunities and Challenges. Workshop on Sustainable Computer Systems (HotCarbon), 2024.
- Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren. Building Socially-Equitable Public Models. International Conference on Machine Learning (ICML), 2024.
- Pengfei Li*, Yejia Liu*, Jianyi Yang, Shaolei Ren. Towards Socially and Environmentally Responsible AI. Ethical Systems and Architecture Design Workshop at ASPLOS (HotEthics, ASPLOS), 2024.
- Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren. Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces. EDGE AI Research Symposium (TinyML), 2024.
- Weiyuan Wu, Pei Wang, Yi Xie, Yejia Liu, George Chow, Jiannan Wang. Web Connector: A Unified API Wrapper to Simplify Web Data Collection. Very Large Data Bases (VLDB), 2023.
- Yejia Liu, Shijin Duan, Xiaolin Xu, Shaolei Ren. MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption. EDGE AI Research Symposium (TinyML), 2023.
- Yejia Liu*, Wang Zhu*, Shaolei Ren. Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning. Conference on Neural Information Processing Systems (NeurIPS), 2022.
- Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu. LeHDC: Learning-Based Hyperdimensional Computing Classifier. Design Automation Conference (DAC), 2022.
- Lampros Flokas, Weiyuan Wu, Yejia Liu, Jiannan Wang, Nakul Verma, Eugene Wu. Complaint-Driven Training Data Debugging at Interactive Speeds. ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
- Yejia Liu, Weiyuan Wu, Lampros Flokas, Jiannan Wang, Eugene Wu. Enabling SQL-based Training Data Debugging for Federated Learning. Very Large Data Bases (VLDB), 2022.
- Yejia Liu, Oliver Schulte, Chao Li. Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts. Machine Learning and Data Mining for Sports Analytics Workshop at ECML-PKDD (MLSA), 2018.
Teaching Experience
- Teaching Assistant — CS 008, Introduction to Computing, UC Riverside Winter 2025
- Head Teaching Assistant — CS 100, Software Construction, UC Riverside Fall 2024
- Head Teaching Assistant — CS 100, Software Construction, UC Riverside Spring 2023
- Teaching Assistant — CS 008, Introduction to Computing, UC Riverside Spring 2022
- Teaching Assistant — CS 009P, Introduction to Programming, UC Riverside Fall 2021
- Teaching Assistant — CS 153, Design of Operating Systems, UC Riverside Winter 2021