Friday, October 31 2025, All day The Georgia Center THE GEORGIA STATISTICS DAY CONFERENCE SERIES UGA Welcomed Academic Industry Partners for Georgia Statistics Day The purpose of this event is to promote interdisciplinary research within the flagship institutions of the state of Georgia. Our conference will enable junior researchers in the Southeast region of the United States, including graduate students, to present their work, to see state of the art developments in research on statistics and related scientific areas, and to interact with some of the key players in the area. Georgia Statistics Day puts emphasis on mentoring of junior researchers and on interaction between senior and junior researchers. Please contact conference organizers if you would like to attend the conference. Please upload your abstracts here: Brochure - Coming Soon Registration - Coming Soon GA Statistics Day Vision Policy Organizers Abhyuday Mandal, University of Georgia Date and Location Friday 10/31/2025 The Georgia Center for Continuing Education & Hotel1197 South Lumpkin Street, Athens, GA 30602 Keynote Speaker Liza Levina, Collegiate Professor of Statistics at the University of Michigan Title: Towards Interpretable and Trustworthy Network-Assisted Prediction Abstract: When training data points for a prediction algorithm are connected by a network, it creates dependency, which reduces effective sample size but also creates an opportunity to improve prediction by leveraging information from neighbors. Multiple prediction methods on networks taking advantage of this opportunity have been developed, but they are rarely interpretable or have uncertainty measures available. This talk will cover two contributions bridging this gap. One is a conformal prediction method for network-assisted regression. The other is a family of flexible network-assisted models built upon a generalization of random forests (RF+), which both achieves competitive prediction accuracy and can be interpreted through feature importance measures. Importantly, it allows one to separate the importance of node covariates in prediction from the importance of the network itself. These tools help broaden the scope and applicability of network-assisted prediction to practical applications. Liza Levina - Website
