Anant Mittal
Anant Mittal is a Ph.D. student at the Paul G. Allen School of Computer Science & Engineering, University of Washington, advised by Prof. James Fogarty. He also holds a graduate research assistant position at the eScience Institute, Scientific Software Engineering Center (SSEC.) His research interests include designing and building interactive systems for real-world human-computer interaction impact and evaluating them through mixed methods. His Ph.D. focuses on building systems for communication and collaboration in settings where multiple stakeholders have different roles. He has presented several papers and posters at conferences and given invited talks.
Sessions
Generative AI systems built upon large language models (LLMs) have shown great promise as tools that enable people to access information through natural conversation. Scientists can benefit from the breakthroughs these systems enable to create advanced tools that will help accelerate their research outcomes. This tutorial will cover: (1) the basics of language models, (2) setting up the environment for using open source LLMs without the use of expensive compute resources needed for training or fine-tuning, (3) learning a technique like Retrieval-Augmented Generation (RAG) to optimize output of LLM, and (4) build a “production-ready” app to demonstrate how researchers could turn disparate knowledge bases into special purpose AI-powered tools. The right audience for our tutorial is scientists and research engineers who want to use LLMs for their work.