Hyejun Jeong
PhD Student in Computer Science
University of Massachusetts Amherst
Email: hjeong@umass.edu
Education
Ph.D. in Computer Science (2023 - Present)
University of Massachusetts Amherst, College of Information and Computer Sciences
Research Focus: Privacy-Preserving Machine Learning, Federated Learning, Trustworthy AI
M.S. in Computer Science (2021 - 2023)
Sungkyunkwan University (SKKU), South Korea
Thesis: Optimizing Resource Utilization in Multi-tenant Cloud Environments
B.S. in Computer Science (2015 - 2019)
Stony Brook University (SBU), New York
Research Experience
Graduate Research Assistant (2023 - Present)
University of Massachusetts Amherst
- Developing privacy-preserving techniques for federated learning systems
- Working on bias detection and mitigation in large language models
- Researching trustworthy AI methodologies and interpretable ML systems
Graduate Research Assistant (2021 - 2023)
Sungkyunkwan University, South Korea
- Conducted research on cloud computing resource optimization
- Published technical reports on multi-tenant environment efficiency
Publications
Technical Reports
- Optimizing Resource Utilization in Multi-tenant Cloud Environments
Hyejun Jeong, et al.
Technical Report, 2023
Conference Presentations
- Research Poster Presentation (as Mentor)
Academic Conference/Workshop
View Poster PDF
See Publications page for complete list.
Research Interests
- Machine Learning: Federated learning, privacy-preserving algorithms
- Natural Language Processing: Large language models, bias detection and mitigation
- AI Safety: Trustworthy AI, model interpretability
- Data Privacy: Differential privacy, secure computation
Technical Skills
Programming Languages: Python, Java, C++, JavaScript
Machine Learning: PyTorch, TensorFlow, scikit-learn
Tools: Git, Docker, Linux, HPC systems
Areas: Federated Learning, NLP, Computer Vision, Cloud Computing
Academic Service
Student Volunteer
- University of Massachusetts Amherst CS Department events
Professional Development
- 2024: Attending workshops on AI Safety and Trustworthy ML
- 2023: Transitioned from industry-focused research to academic research
Last updated: December 2024