Friday, May 19 at 12:00pm to 1:00pm
Center for BrainHealth
2200 West Mockingbird Lane Dallas, TX 75235
Statistical Machine Learning and Psychometrics
The Center for BrainHealth invites scientists to share their scientific study with students and other researchers at the BrainHealth Frontiers Lunch Lectures. The lectures are heavily science focused and are not intended for a lay audience.
Friday, 5/19/2023 at noon
Registration is free. Please Register to attend this in-person and virtual event.
Richard Golden, PhD
UT Dallas
Structural causal models (SCMs) are powerful tools for identifying confounding factors and may, in some cases, permit causal inferences from observational data sets. In this talk, the relevance and use of SCMs for supporting statistical data analyses and machine learning methods for health services related research areas are discussed.
Dr. Richard Golden is currently serving on the expert Panel on Causal Modeling for the Department of Veterans Affairs. He has served on the Editorial Boards for the machine learning journal Neural Networks and the Journal of Mathematical Psychology for more than a decade. Dr. Golden is the author of the advanced machine learning text: Statistical Machine Learning: A unified framework.
The host for this event is Dr. Daniel Krawczyk.
The spring 2023 season will be in person at Center for BrainHealth (lunch provided) and live-streamed. Register free of charge for the season and join us for as many talks as you are able.
Center For BrainHealth
2200 West Mockingbird Lane
Dallas, TX 75235
UTD strives to create inclusive and accessible events in accordance with the Americans with Disabilities Act (ADA). If you require an accommodation to fully participate in this event, please contact the event coordinator (listed below) at least 10 business days prior to the event. If you have any additional questions, please email ADACoordinator@utdallas.edu and the AccessAbility Resource Center at accessability@utdallas.edu.
Faculty & Staff, Undergraduate Students, General Public, Prospective Students, Graduate Students