Andrew Neeser earned a bachelor’s degree in computer science from Virginia Tech and returned to the university to pursue a master’s degree. 

Advised by Naren Ramakrishnan, his research focuses on Retrieval-Augmented Generation (RAG), a powerful approach to grounding large language models in real-world documents. Specifically, he is researching ways to optimize and enhance RAG pipelines, improving how these models retrieve and generate contextually relevant information.

“My interest in RAG began with my senior capstone project during my undergraduate studies, when my team and I developed a RAG pipeline and user interface allowing users to upload their documents and interact with AI conversationally,” Neeser said. 

That experience sparked his passion for exploring new applications of RAG and led him to the Sanghani Center, “where I knew I would be at the forefront of AI research,” he said. 

“I was already deeply involved in research in AI, so transitioning into the Sanghani Center felt like a natural step toward more structured and impactful work in this field,” Nesser said. “I love the chance to work on real-world projects and collaborate with talented teams on cutting-edge research. I also value the flexibility the Sanghani Center offers to explore topics that truly interest me, which makes my work both engaging and rewarding.”

In May, his paper, "Wireless Knowledge Grounding in Smaller LLMs using Retrieval Augmented Generation and Fine-Tuning,"  will be presented at the Machine Learning for Communications and Networking Track within the IEEE Symposium on Advances in Communications (SAC). 

After graduation, projected for May 2025, Neeser hopes to continue advancing RAG research and developing real-world applications for AI tools. 

“I am also very interested in the entrepreneurial side of technology and hope to start my own company in the future,” he said.