Ishika Khandelwal
Ishika is a graduate Student at the UW - Seattle, specializing in Data Science. She is currently working as a Research Graduate Scholar at the Scientific Software Engineer Center at eScience Institute, UW. Before this, Ishika worked as a Decision Analytics Associate at ZS Associates, India wherein she played a pivotal role in informing strategic decision-making processes and driving impactful outcomes for a pharma client, ultimately contributing to the success of their business objectives. Her passion lies at the confluence of Software Development Engineering (SDE) and Data Science best practices and is driven by a relentless pursuit of knowledge and a thirst for mastery in these dynamic disciplines.
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.