Sri Ramm

I think about AI beyond just the models themselves. What interests me most is how research turns into systems people can actually rely on whether that is in healthcare, telecom, or other real-world settings where accuracy, scale, and reliability matter.

Right now, I am deeply interested in Large Language Models, retrieval-augmented generation, and domain specific AI systems. I enjoy learning how these models work, how they can be adapted to specialized use cases, and how to build practical workflows around them instead of treating them like black boxes.

My background is a little unconventional. Before graduate school, I spent over two years in telecom production environments, working with real systems, noisy data, and performance issues that could not simply be ignored. That experience shaped the way I think about machine learning today not just as modeling, but as something that must be robust, useful, and deployable.

At UMD, I am pursuing my MS in Applied Machine Learning with a 4.0 GPA. Along the way, I have been exploring projects in local privacy-first RAG systems, applied deep learning, and domain specific AI. I am especially drawn to work that connects strong technical foundations with real user impact.

What drives me most is the idea that the next wave of AI will not come just from bigger models, but from better systems models grounded in real data, designed for specific domains, and built in ways that make them trustworthy and practical.

I am looking for opportunities to work with teams building thoughtful, real-world AI systems.