About me
I am a computer science student at the University of Texas at Austin with a focus on large language models (LLMs), machine learning, RAG vector databases, and data privacy. Iam passionate about tackling complex challenges through interdisciplinary collaboration and working with diverse teams to push the boundaries of what technology can accomplish. When I am not immersed in research, you can find me swimming, enjoying the outdoors, or exploring new places.
Honors
Regeneron Science Talent Search Scholars 2022
- Top 300 finalist in the national research competition by the Society for Science.
Research Experience
Retrieval Augmented Generation (RAG) Vector Database | Research Advisor Prof. Dixin Tang | 01/2025 - present
- Conduct performance measurements for various RAG vector databases.
- Develop a new vector database to enhance LLM's performance by leveraging chat history.
Research Assistant (NSF CNS-2029569) | AI Networking and Security Lab | 05/2020 - 08/2023
- Developed a privacy-preserving federated machine learning framework to model the COVID-19 pandemic's spread and created fine-grained vulnerability maps.
- Proposed and designed an adaptive worker selection algorithm to improve model accuracy with limited and imbalanced data.
- Implemented a differential privacy scheme while maintaining model accuracy.
- Developed a crowdsourcing mobile app for users to anonymously share COVID-19 symptoms.
- Utilized Google Firebase to manage user data and interactions on the client side, and implemented a Google BigQuery data warehouse to analyze collected data and identify trends and patterns.
- Published and presented first-author papers in IEEE International Conference on Communications and IEEE Internet of Things, Ad Hoc and Sensor Network Technical Committee Newsletter. Contributed to NSF annual report and system demonstration.
Teaching Experience
Teaching Assistant | CS 330E (Software Engineering) and CS 311 (Discrete Math) | 08/2024 - Present
- Supported 150 students on software engineering projects.
- Led weekly discussion sessions, graded assignments and exams, and provided individualized support during office hours.
Publications
- Jeffrey Chen, R. Chen, X. Zhang, L. Li, Y. Gong, Y. Guo, L. Ni, and M. Pan, "Location Privacy-Preserving COVID-19 Symptom Map Construction via Mobile Crowdsourcing for Proactive Constrained Resource Allocation," IEEE Internet of Things, Ad Hoc and Sensor Network Technical Committee Newsletter, No. 13, 2022.
- Jeffrey Chen, R. Chen, X. Zhang, and M. Pan, "A Privacy-Preserving Federated Learning Framework for COVID-19 Vulnerability Map Construction," IEEE Int. Conf. on Communications, June 14-23, 2021.
- Jeffrey Chen, R. Chen, X. Zhang, and M. Pan, "An Ensemble Machine Learning Framework for COVID-19 Vulnerability Map Construction via Privacy Preserving Crowdsourcing," International Conference on Digital Healthcare and Technology, London, May 24-25, 2021.
- R. Chen, L. Li, Jeffrey Chen, R. Hou, Y. Guo, and M. Pan, "COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing," IEEE Global Comm. Conf. Dec. 7-11, 2020.