On Monday June 17 there will be a full-day, interactive short course in which participants will learn about large language models. Details are provided below. Note that registration for the short course is separate from registration for the conference. A participant may register for the short course only, or the short course in addition to the main conference. Pricing details may be found on the registration page.
Introduction to Large Language Models
As statisticians, we often work with standard data structures like data frames, which contain numerical and categorical variables. These structures commonly comprise millions of rows and thousands of columns. However, there exists a multitude of other data structures that aren't universally familiar to statisticians, such as text data. Over the last decade, deep learning methods have continuously evolved, particularly in handling text data. Among the most recent advancements are large language models. In this short course, we aim to integrate large language models into our modeling toolkit. Initially, we'll introduce fundamental concepts and then delve into hands-on examples for practical application. Our learning journey will include setting up a computational environment on a cloud system, a process we'll navigate together. To participate, all you'll need is a laptop equipped with an internet browser.
About the Instructors
Ming Li is currently a Staff Data Scientist at Coupang. He was a Director of Data Science in PetSmart, a Science Manager in Amazon, Data Scientist in Walmart and Statistical Leader in GE Global Research Center. He has visited 20 ASA Chapters to teach the ASA traveling course in data science, machine learning and deep learning during 2019, 2020 and 2021. He also organized and presented the 2018 JSM Introductory Overview Lecture: Leading Data Science: Talent, Strategy, and Impact. He was the Chair of Quality & Productivity Section of ASA. With 10+ years' experience in data science and machine learning, he has trained and mentored numerous junior data scientists with diversified backgrounds such as statistician, software developer, database programmer, and business analyst. He was also an instructor of Amazon's internal Machine Learning University and the recipient of Amazon's Best Science Mentor Award. He holds a Ph.D. in Statistics from Iowa State University.
Xiaoda Liu is currently a Staff Machine Learning Engineer at Coupang. He was an Applied Scientist at Amazon. With over six years of experience, he has been developing large scale machine learning and deep learning systems for retail and advertising sectors in the companies. His expertise lies in the application of advanced AI techniques to solve complex industry problems. He has mentored numerous data scientists and software engineers. He serves as reviewer for deep learning journals including Neurocomputing, IEEE Transactions on Neural Networks and Learning Systems. He holds a Ph.D. in Petroleum Engineering from Texas A&M University.