I study how models encode semantic meaning that remains stable under perturbations. My work spans robust representation learning for image watermarking, auto-augmentation, and identifying invariant latent features in LLMs for attribution and forensics.
I'm a doctoral researcher in Information Science & Technology at the University of Nebraska Omaha. My dissertation centers on invariant representation learning and its applications to robust image watermarking and LLM robustness.
Broadly, I design systems that remain stable under content-preserving transformations: geometric/photometric augmentations for images and lexical/structural paraphrases for text. I care about what an AI model knows versus how it encodes it.
IEEE Transactions on Artificial Intelligence 2025
Prompt-based LLM watermarking framework that embeds detectable signals in model responses without modifying weights or data. Evaluated watermark generation and detection using instruction-tuned LLMs. The figure above is an overview of our prompting strategy.
International Conf. on Computational Science & Computational Intelligence 2023
Watermark embedding and extraction method resilient to geometric and photometric attacks. Utilizes ViT-based cross-attention to align invariant domain features for robust watermark decoding. The figure above shows an overview of our proposed franework.
Links to papers in press will be updated as they become available.
