Context-Enriched Dynamic Graph Word Embeddings for Robust NLP Applications
Tran, T. X., Himes, R. E., & Tran, H. A. (2025). Context-Enriched Dynamic Graph Word Embeddings for Robust NLP Applications. SoICT 2024, 49, 463-472.
CV
Education
- Ph.D in Computer Science, University of Maryland PSSG Group, 2030 (Expected)
- M.S.E in Computer & Information Science Concentration in Systems, University of Pennsylvania, 2026
- B.S in Computer Science Minor in Computer Engineering, Pennsylvania State University, 2024
Work experience
- AI/ML Research Engineer, Lockheed Martin Enterprise Operations
- Applied AI Mission Management Team
- Presented our Team’s Work at the MORS conference
- Associate Software Engineer, Lockheed Martin
- Advanced Extremely High Frequency Team
- Full Stack SWE focused on backend C# development
Skills
- C#
- Software Testing
- C
- Python
- Machine Learning
- Computer Networking
- Graph Neural Networks
- Natural Language Processing
- Computer Vision
Publications
Leveraging Dynamic Graph Word Embedding for Efficient Contextual Representations
Himes, R.E., Tran, HA., Tran, T.X. (2025). Leveraging Dynamic Graph Word Embedding for Efficient Contextual Representations. In: Buntine, W., Fjeld, M., Tran, T., Tran, MT., Huynh Thi Thanh, B., Miyoshi, T. (eds) Information and Communication Technology. SOICT 2024. Communications in Computer and Information Science, vol 2352. Springer, Singapore. https://doi.org/10.1007/978-981-96-4288-5_20