POST-EVENT UPDATE: With the meeting now come and gone, I want to thank all of you who showed up. I think it everything went really well and there were quite a few interesting talks, as well as a successful poster session. Hopefully we will continue having these meetings in the future.
You find more photos from the event here.
Thank you for your interest in the Fall 2018 meeting of the South Carolina Chapter of the ASA. This event will be held on Saturday, October 13 in the the Madren Conference Center at Clemson University.
UPDATE: Registration is now CLOSED. Thanks to those of you who registered. See you soon!
A short program for the workshop is here. The auditorium will open at 10:00 am. Alex McClain, President of the SC-ASA, will deliver some opening remarks at 10:15 am. The event is scheduled to run through 4:30 pm.
The abstracts for the poster session can be found here.
Invited Speakers (Click each title for an abstract):
- Alexander Alekseyenko, Medical University of South Carolina Department of Public Health Sciences, “Microbiome data science”
- Joe Bible, Clemson University Department of Mathematical Sciences, A joint model for longitudinal data with informative cluster size and informative observation time: Application to analyses of medical record data”
- Donjun Chung, Medical University of South Carolina Department of Public Health Sciences, A statistical framework for the integration of GWAS results for multiple diseases with literature mining data
- Karl Gregory, University of South Carolina Department of Statistics, “Optimal estimation of sparse high-dimensional additive models”
- Chris McMahan, Clemson University Department of Mathematical Sciences, Bayesian generalized additive regression for group testing data
- Edsel Peña, University of South Carolina Department of Statistics, “Confidence and credible regions”
- Yuan Wang, University of South Carolina Department of Epidemiology and Biostatistics, “Topological permutation tests in seizure location”
- Feifei Xiao, University of South Carolina Department of Epidemiology and Biostatistics, “Copy number variation detection with complex genetic data”
