Upcoming events
2023 ASA GA Chapter Winter Lecture Series
Speaker: Richard A. Davis, Professor of Statistics, Columbia University
Date: December 7, 2023
Time: 4:00pm EST
Location: Virtual (Zoom link: https://zoom.us/j/99347646840)
Speaker: Vladimir Dragalin, Vice President, Scientific Fellow, Janssen (Pharmaceutical Companies of Johnson and Johnson)
Date: December 14, 2023
Time: 4:00pm EST
Location: Virtual (Zoom link: https://zoom.us/j/99347646840)
Past events
Georgia Statistics Day 2023
You are cordially invited to the 2023 Georgia Statistics Day, a one-day workshop that brings together top researchers across Georgia to foster collaboration and innovation in statistics, data science, and related disciplines.
Date: October 9 (Monday), 2023
Time: 8:45 AM - 5:00 PM
Location: Georgia Tech Exhibition Hall (map) Please see the flyer for the activities and registration information: https://sites.gatech.edu/gsd2023/
Deadline for registration: October 5, 2023.
2023 BRADLEY LECTURE Dr. Dylan Small Universal Furniture Professor of Statistics and Data Science & Department Chair at the Wharton School, University of
Pennsylvania.
Title: Protocols for Observational Studies: Methods and a Gun Violence Prevention Study Date: April 14 (Friday), 2023
Time: 4:00 PM
Location: Founders Memorial Garden House, University of Georgia Please see the flyer for the agenda and further information: https://www.stat.uga.edu/events/content/2022/2023-bradley-lecture
2023: Third Annual Workshop: Emerging Data Science Methods for Complex Biomedical and Cyber Data Date: March 16-17 (Thursday-Friday), 2023 Time: All day
Location: Georgia Cyber Center, Augusta, GA Please visit the page for further information:
https://www.augusta.edu/mcg/dphs/workshop3/index.php#
2022 WINTER LECTURE SERIES
Dr. Vijay Nair
Head, Advanced Technologies for Modeling, Wells Fargo & D. A. Darling Professor Emeritus, University of Michigan
Title: Stability and Approximability of Deep ReLU Networks in Statistical Learning
Date: December 14 (Wednesday), 2022
Time: 4:00 PM
Location: Virtual (by Zoom) Please see the flyer for more information: https://www.stat.uga.edu/sites/default/files/ASA_GA_Winter_Lecture_2022_Vijay%20Nair.docx.pdf
Dr. Sujit Ghosh
Professor and Interim Department of Statistics Head at North Carolina State University
Title: Possible Hazards of Some Popular Hazard Rate Models
Date: December 8 (Thursday), 2022
Time: 4:00 PM
Location: Virtual (by Zoom) Please see the flyer for more information: https://www.stat.uga.edu/sites/default/files/ASA_GA_Winter_Lecture_2022_Ghosh_Sujit_0.pdf
2022 Bradley LECTURE
Dr. Jianqing Fan
Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at Princeton University
Title: Stability and Approximability of Deep ReLU Networks in Statistical Learning
Date: April 29 (Friday), 2022
Time: 4:30 PM
Location: Athens Botanical Garden, Garden Club Terrace Room
The Annual Bradley Lecture Series are hosted by the University of Georgia Department of Statistics and the Statistics Club in honor of former faculty member Dr. Ralph A. Bradley.
Please see the full agenda and the flyer for further information:
https://www.stat.uga.edu/events/content/2020/2022-bradley-lecture
https://www.stat.uga.edu/sites/default/files/Bradley_2022_Flyer.pdf
2021 WINTER LECTURE SERIES
Dr. Sharon Lohr
Professor Emerita at Arizona State University and Statistical Consultant
Title: Sweatshops, Smallpox, and Statistics: Florence Kelley in 1890s Chicago
Date and time: December 14 (Tuesday), 2021 at 4:00 PM
See the flyer for further information:
https://www.stat.uga.edu/sites/default/files/ASA_GA_Winter_Lecture_2020_2_Sharon_Lohr%20%281%29.pdf
Dr. Jeffrey Rosenthal
University of Toronto
Title: Monte Carlo Algorithms, from Theory to Practice
Date and time: December 7 (Tuesday), 2021 at 4:00 PM
See the flyer for further information: https://www.stat.uga.edu/sites/default/files/ASA_GA_Winter_Lecture_2020_1_Jeff_Rosenthal%20%281%29.pdf
2021 Georgia Statistics Day
Emory University, October 11th, 2021.
The conference website: https://gsd2021-emory.github.io/
2021 Summer ASA Traveling Course
Title: Introduction to Data Science, Machine Learning and Deep Learning (in R and Python)
Instructors: Dr. Hui Lin (Netlify) and Dr. Ming Li (Amazon)
Date: Friday and Saturday, August 6 - 7, 2021
Time: 1:00 - 4:30 pm
Location: Online. Further information about the course and registration is available on the website https://asageorgiachapter.regfox.com/introduction-to-data-science-machine-learning-and-deep-learning
Course Overview:
This short course will provide an overview of using R and Python for some of the most popular machine learning and deep learning models in real-world data science applications in the cloud environment. The sessions will step through the basic theoretical concepts behind those models and mainly focus on applications. Students will learn the motivation and use cases of these models through hands-on exercises. The course's main topics include: big data cloud environment, tree-based models, regularization methods, feedforward neural network, convolutional neural network, and recurrent neural network. The course syllabus, program, and materials are available on the website https://course2021.scientistcafe.com/.
Set-up and prerequisites:
This course is designed for audiences with a statistics education background, and it bridges the gap between traditional statisticians and data scientists. No software download or installation is needed, and everything is done through the internet browser with hands-on sessions in Databrick's cloud environment and Colab. Knowledge of basic R or Python is recommended. The instructors will provide a notebook for both languages. The short course will be delivered online with two 3.5-hour sessions on two consecutive days. The link to the online event will be provided to participants before the start of the course.
About the instructors:
Ming Li is currently a senior research scientist at Amazon and adjunct instructor of the University of Washington. He 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 a few 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 is also an Instructor of Amazon's internal Machine Learning University and the recipient of Amazon's Best Science Mentor Award. He holds Ph.D. in Statistics from Iowa State University.
Hui Lin is currently a Quant Researcher at Google. Before Google, Hui held different roles in data science. She was the head of data science at Netlify, where she built and led the data science team, and a Data Scientist at DuPont, where she did a broad range of predictive analytics and market research analysis. She is the co-founder of Central Iowa R User Group, blogger of https://scientistcafe.com/, and 2018 Program Chair of ASA Statistics in Marketing Section. She enjoys making analytics accessible to a broad audience and teaches tutorials and workshops for data science practitioners. She holds MS and Ph.D. in statistics from Iowa State University.
UGA Department of Statistics Colloquium
Guest: Michael Hamada, Statistical Sciences Group, Los Alamos National Laboratory
Date/Time: Thursday, April 22, 2021 @ 4pm ET via Zoom/virtual
See flyer for details
ASA Georgia Chapter Fall Meeting
Emory University, November 13th, 2018
Living the Reproducible Life with Dr. Hadley Wickham (Chief Scientist, RStudio)
October 16th, 2018
Direct Link to Video Recording
Direct Link to Slides
2018 Spring ASA Traveling Course
Title: An Introduction to the Analysis of Incomplete Data
Instructor: Professor Ofer Harel (Department of Statistics, University of Connecticut)
Date: Saturday, March 24th
Time: 8:30am-5pm
Location: ISyE Main Building, Room #228 Georgia Institute of Technology 755 Ferst Drive NW Atlanta, GA 30332
Course Overview: Missing data is a common complication in applied research. Although most practitioners are still ignoring the missing data problem, numerous books and research articles demonstrate that dealing with it correctly is very important. Biased results and inefficient estimates are just some of the risks of incorrectly dealing with incomplete data. In this course, we will introduce incomplete data vocabulary and present problems and solutions to the missing data issue. We will emphasize practical implementation of the proposed strategies including discussion of software to implement procedures for incomplete data.
About the instructor: Ofer Harel, Ph.D. is a professor in the Department of Statistics, the Center for Public Health and Health Policy (CPHHP) and a FORMER Principal investigator (PI) at the Institute for Collaboration on Health, Intervention, and Policy (InCHIP) at the University of Connecticut. Through his career Dr. Harel developed his methodological expertise in the areas of missing data techniques, diagnostic tests, longitudinal studies, Bayesian methods, sampling techniques, mixture models, latent class analysis and statistical consulting. Dr. Harel was part of numerous federal grants as principle investigator (PI), Co-PI and Biostatistician. He is an associate editor for Statistics in Medicine, Sankhya, the Indian Journal of Statistics, Series B and on the editorial board of AIDS and Behavior and The Open Medical Informatics Journal. Through his work, Dr. Harel has been involved with a variety of research fields including, but not limited to single-cell genomics, HIV prevention, Alzheimer’s, cancer, diabetes, and alcohol and drug abuse prevention.
12/1/2017 - Webinar Series #1
Title: Introduction to Graphical Models and Application to Brain Networks
Presenter: Dr. Suprateek Kundu, Department of Biostatistics and Bioinformatics, Emory University
Webinar Archive: https://emory.adobeconnect.com/_a812723154/p0i7szvysien/
Summary: In this first ever ASA GA Chapter Webinar, Dr. Kundu is going to cover the basics of graphical modeling approaches and their application to brain networks. Graphical models provide useful tools to compute a network of dependencies among a set of pre-defined nodes and there has been a rich literature in graphical modeling with important applications in genetics, finance and more recently brain imaging. Dr. Kundu will focus on two key approaches: Gaussian graphical models for continuous data, and Ising models for binary data. He will highlight the advantages and pitfalls of these approaches and discuss some recent approaches for high dimensional graphical modeling. In the second half of the talk, Dr. Kundu will elucidate the applications of graphs for computing brain networks using fMRI data, an important and rapidly developing research area. The presentation will end with possible challenges for computing brain networks and some open research questions.
Speaker Bio: Dr. Kundu is an Assistant Professor in the Department of Biostatistics at Emory University. He is also a core faculty member in the Center for Biomedical Imaging Statistics (CBIS) , and a member of The Center for Visual and Neurocognitive Rehabilitation at VA, Emory. Dr. Kundu's research interests include developing flexible Bayesian approaches motivated by challenging biomedical applications in genomics and neuroscience. His experience includes non- and semi-parametric Bayesian approaches, density estimation and regression, high dimensional variable selection and graphical models. One of the recent focus areas is to incorporate disparate data types as well as prior knowledge to achieve more robust and dependable statistical estimation approaches. Examples include fusing multimodal structural and functional imaging data to infer brain networks, or jointly analyzing resting state and task-based brain networks, or developing feature selection methods for high dimensional genetic studies incorporating prior information on gene networks. Professor Kundu's web page: sites.google.com/view/suprateek
11/17/2017 - 6th Annual KSU R Day 2017
The sixth annual “KSU R Day” was held by Kennesaw State University’s Department of Statistics and Analytical Sciences to celebrate undergraduate and graduate statistics students’ work in R and serve as a regional meeting for the R community.
10/9/2017 - Georgia statistics day
Georgia Statistics Day was a one-day conference dedicated to interdisciplinary research on theoretical and applied data science hosted at the Rollins School of Public Health at Emory University. The Georgia ASA Chapter sponsored an award for the winner of the student poster presentation.
