AIMI Research Meeting: Meerkat: Interactive Data Systems For Unstructured Data & Foundation Models - Arjun Desai, MS; Karan Goel, MS; Sabri Eyuboglu, MS

Abstract: Unstructured data (e.g. images, videos, text documents, etc.) are ubiquitous in today's digital world. However, the analysis of such data using traditional data science tools can be quite challenging. Foundation models (FMs) have shown that they extract semantically meaningful information from diverse types of unstructured data, but they can be imprecise, brittle, and difficult to control. We’re excited to introduce Meerkat, a Python library that teams can use to interactively wrangle their unstructured data with foundation models. In this talk, we will explore how Meerkat can facilitate collaboration between ML engineers and medical experts. We will discuss how Meerkat makes it easier to work with unstructured data and FMs, learn how to build user interfaces with Meerkat, and dive into applications in healthcare and the sciences. Interested in building with Meerkat? Check out our website (http://meerkat.wiki) and join our Discord (https://discord.gg/pw8E4Q26Tq)!

Bio: Arjun Desai is a 4th-year EE PhD student working with Akshay Chaudhari and Chris Ré. He is interested in how signal processing principles can improve robustness, efficiency, and scalability in machine learning. He is excited about how these methods can help build scalable deployment and validation systems for challenging applications in healthcare and the sciences. He is a recipient of the NDSEG and NSF GRFP Fellowships.

Bio: Karan Goel is a 5th year CS PhD student at Stanford advised by Chris Ré. He is interested in sequence modeling techniques for building large-scale foundation models, as well as problems that arise due to the deployment of ML models to practice. As part of the Meerkat project, he thinks about how the application of FMs will change systems for data science and engineering. He is a recipient of the Siebel Foundation Scholarship.

Bio: Sabri Eyuboglu is a 3rd year PhD student advised by Chris Re and James Zou. He is broadly interested in how we can bring machine learning to bear in challenging applied settings like medicine and the sciences. To that end, he’s recently been working on data management tools that help practitioners better understand their data.He is supported by the National Science Foundation GRFP.

Attendance is open to the Stanford community. If you would like to attend in-person or on Zoom, please contact the AIMI Center at aimicenter@stanford.edu.