The 2023 Alaska Chapter of the American Statistical Association workshops (a hybrid event) will be held on Thursday-Friday, June 22-23, in Anchorage, Alaska and online.

About this event

Course 1: From R Markdown to Quarto

Thursday, June 22, 2023

Instructor: Andrew Bray

Course description: This workshop is designed for those who want to take their R Markdown skills and expertise and apply them in Quarto, the next generation of R Markdown. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. In this workshop you will learn how to apply your reproducible authoring skills to the Quarto format and learn about new tools and workflows for authoring with Quarto in RStudio. You will learn to create static documents as well as slide presentations. The workshop will also introduce you to Quarto projects which you can use to build websites and write blogs and books. Finally, you will learn various ways to deploy and publish your Quarto projects on the web.

About the instructors

Andrew Bray is an Associate Teaching Professor in the Department of Statistics at UC Berkeley where he develops and teaches courses in statistics and data science. His research interests include statistical computing, data privacy, and applications of statistical models to environmental science. He is one of the authors of the infer R package for resampling based inference and an enthusiastic user of all things R Markdown / Quarto.

Course 2: Deep Learning in Statistics

Friday, June 23, 2023

Instructor: Annie Qu , Xiao Wang, Edgar Dobriban

Course description: This short course is for those who are new to machine learning and interested in understanding deep learning models. It is for those who want to become familiar with the core concepts behind these learning algorithms and their successful applications. It is for those who want to start thinking about how machine learning and deep learning might be useful in their research, business or career development. This one-day short course will provide an overview of statistical machine learning and deep learning methods. Topics include classical methods as well as modern techniques including basic machine learning tools, supervised and unsupervised learning, deep neural networks, computational algorithms and software of deep learning, and various applications in deep learning. 

About the instructors

Annie Qu is Chancellor’s Professor of Statistics at the University of California Irvine. She received her Ph.D. in Statistics from Penn State in 1998. Her research interests include machine learning, medical imaging, recommender system, natural language processing, personalized medicine, longitudinal/correlated data analysis, missing data, model selection and nonparametric models. Dr. Qu received an NSF Career award, and is a fellow of the Institute of Mathematical Statistics and of the American Statistical Association. She is the past Chair of the Statistics Learning and Data Science Section of the American Statistical Association. Currently, she is associate editor for JRSSB, JASA, Statistica Sinica, Electronic Journal of Statistics and Journal of Nonparametric Statistics. She also served as associate editor for Biometrics and Statistical Science in the past. She is the co-editor of JASA Theory and Method.

Xiao Wang obtained his B.S. and M.S. in mathematics from University of Science and Technology of China, and Ph.D. in Statistics from University of Michigan. He is now a Professor in Department of Statistics at Purdue University. His research interests are primarily on machine learning, deep learning, nonparametric statistics, functional data analysis, and reliability, as well as their application in neuroimage and industry. He is a fellow of the Institute of Mathematical Statistics and of the American Statistical Association. He also serves as associate editor of Journal of the American Statistical Association, Technometrics, and Lifetime Data Analysis.

Edgar Dobriban holds a B.A. in mathematics from Princeton University (with highest honors) and a Ph.D. degree in Statistics from Stanford University. He is currently an Assistant Professor of Statistics in the Wharton School of the University of Pennsylvania. His research interests include the statistical analysis of massive datasets, applications of random matrix theory, distributed statistical learning and deep learning. His work has been published in leading journals in statistics, such as the Annals of Statistics, Annals of Applied Statistics, Journal of the Royal Statistical Society, Biometrika. He has developed publicly available software for processing big data using tools from random matrix theory. He received the TW Anderson Prize for best PhD thesis in the theory of statistics from Stanford University in 2017, the NSF Career Award in 2021, a junior research award at the ICSA 2022 China Conference, a Bernoulli Society New Researcher Award (2023), a Sloan Research Fellowship in Mathematics (2023) and a Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (2023).

The following are links pertinent to this year's meeting (links to files or to other web locations will become active when they become available):

Link Description
Information and 2-Day Registration Registration link for the eventbrite site.

All registrations should be made through this site. Payment for registration fees by credit card can only be made through the eventbrite site. Details for payment by check or on-site at the meeting are also provided.

Registering through this site will require a creation of a profile the first time you use the system (it files by your email address), so if you used this system in previous years your email is likely already registered.

Note that meeting attendees need to register via the on-line registration process, if you can not register on-line, contact Jiaqi Huang (907-267-2327) for assistance. Also direct all registration questions to Jiaqi.