The IRIS Lab is dedicated to advancing the frontier of intelligent information systems research. We explore the fascinating challenge of teaching machines to move beyond simple data retrieval toward genuine understanding, reasoning, and natural interaction with knowledge repositories.

Our work sits at the convergence of Information Retrieval (IR), Natural Language Processing (NLP), and Large Language Models (LLMs), creating systems that evolve alongside users’ increasingly sophisticated information needs. Rather than treating search as a static process, we envision it as an adaptive dialogue between humans and machines.

At the IRIS Lab, we blend the foundations of traditional IR with cutting-edge neural architectures to develop systems that don’t just locate information—they enhance comprehension, uncover meaningful connections, and deliver contextually relevant results. A central focus of our research is Retrieval-Augmented Generation (RAG), leveraging the remarkable capabilities of LLMs while ensuring precision, transparency, and reliability.

Through this work, we aim to fundamentally transform the digital information landscape, building technologies that bridge vast, complex data ecosystems and intuitive human understanding. Our efforts carry broad implications across domains where intelligent information access is critical, from science and education to business, healthcare, and governance.

Join us as we push the boundaries of Large Language Models, Retrieval-Augmented Generation, and Neural IR to advance how machines understand and interact with information.

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