Speaker: Dr. Eric Price, of The University of Texas at Austin

Description: Over the past 5 years, large language models (LLMs) have gone from struggling with elementary school math to acing college math and science exams.  How did we get here, and what’s coming next?

Biography: Dr. Eric Price is an Associate Professor in the Department of Computer Science at the University of Texas at Austin since the Fall of 2014.  Eric was awarded a Ph D degree from MIT in 2013.  He was a post-doc at the  Simon Institute for the Theory of Computing in UC Berkeley from 2013 to 2014. He also served as a visiting scientist for Microsoft AI, Google, OpenAI and IBM Research over the years.

Eric’s research explores the fundamental limits of data-constrained computational problems. How efficiently can we recover signals from noisy samples? And how much space do we need to compute functions of large data streams? His work has given algorithms with tight, or near-tight, sample complexities for a variety of such problems, along with corresponding lower bounds.

Time: 6:30 pm

6:30 to 6:35 — Open for participants to network

6:35 to 6:40 — IEEE Life Members and Consultants Network business, followed by speaker introduction

6:40 to 8:00 — Presentation and Q&A

Location: Virtual Zoom meeting

Cost: None

Reservations: Please RSVP using the vTools link https://events.vtools.ieee.org/m/476477.  Registered attendees will be emailed connection instructions in the 24-hour interval prior to the start of the meeting. Look for an email from Kai Wong or IEEE eNotice.

You do not need to be an IEEE Member to register.