Congratulations to Mosarrat Rumman, Bridget Green and Daniel Hinbest on presenting posters at today’s Mindful AI Research Institute (MAIRI) Symposium 2026.
Tag Archives: contrastive learning
Bug Severity Classification Research Published at COMPSAC-SETA 2025
Today at the IEEE COMPSAC Symposium on Software Engineering Technologies & Applications (COMPSAC-SETA 2025) Mosarrat Rumman presented “A Contrastive Learning Approach to Bug Severity Classification with Large Language Model Embeddings” (co-authors Anushka Zaman, Emon Roy, Jeremy Bradbury). This research shows that contrastive fine-tuning for LLMs can improve semantic separation and boost generalization on imbalanced, diverseContinue reading “Bug Severity Classification Research Published at COMPSAC-SETA 2025”
SEER Lab at COMPSAC 2025!
This week SEER Lab’s Prof. Jeremy Bradbury along with MSc students Mosarrat Rumman and Anushka Zaman are attending the 49th IEEE International Conference on Computers, Software, and Applications (COMPSAC 2025). At the conference Mosarrat will be presenting their paper, “A Contrastive Learning Approach to Bug Severity Classification with Large Language Model Embeddings,” at the IEEEContinue reading “SEER Lab at COMPSAC 2025!”