ALIGN-SIM Resources
Nov 1, 2024
·
1 min read
Auburn University
- Released software and data for task-free evaluation of sentence embeddings using five semantic similarity alignment criteria.
- Evaluated 13 classical and LLM-induced encoders and demonstrated that strong downstream benchmark performance can coexist with failures on intuitive semantic alignment tests.
Authors
Postdoctoral Researcher, BridgeAI Lab
I’m a postdoctoral researcher in the BridgeAI Lab at the University of Central
Florida, where I study how language models and AI systems should be evaluated,
selected, and deployed when aggregate benchmark scores do not capture semantic
behavior, user requirements, or deployment constraints. I completed my Ph.D. in
Computer Science and Software Engineering at Auburn University in 2025, advised by
Dr. Sathyanarayanan N. Aakur and co-advised by
Dr. Shubhra Kanti (Santu) Karmaker, with a dissertation
on evaluating semantic and contextual alignment in language models. My current work
combines model evaluation, resource-aware model selection, and scalable inference
into reproducible methods for matching AI models to real-world tasks.