Please join us for the Statistics Seminar scheduled from 11 a.m. to noon on Friday, March 1st.

Our distinguished guest speaker, Xiaotong Shen from the University of Minnesota, will be present a talk on "Boosting Data Analytics with Synthetic Volume Expansion."

The seminar will be conducted in-person in HOS 380; however, virtual attendance is also available via Zoom. Further details can be found below. 

Abstract: 
Synthetic data generation heralds a paradigm shift in data science, addressing the challenges of data scarcity and privacy and enabling unprecedented performance. As synthetic data gains prominence, questions arise regarding the accuracy of statistical methods compared to their application on raw data. Addressing this, we introduce the Synthetic Data Generation for Analytics framework, which applies statistical methods to high-fidelity synthetic data produced by advanced generative models like tabular diffusion models. These models, trained using raw data, are enriched with insights from relevant studies. A significant finding within this framework is the generational effect: the error of a statistical method initially decreases with the integration of synthetic data but may subsequently increase. This phenomenon, rooted in the complexities of replicating raw data distributions, introduces the "reflection point," an optimal threshold of synthetic data defined by specific error metrics. Through three case studies--sentiment analysis, predictive modeling, and inference of tabular data, we demonstrate the effectiveness of this framework.

• Zoom link
• Meeting ID: 912 6500 0523
• Passcode: 38044138

For further information, contact Farhad Shokoohi.