Graduate Assistant, Department of Statistics, University of Connecticut
Boyang Tang is genuinely interested in statistical modeling, predictive analytics, and machine learning, aiming to use these tools to derive meaningful insights from complex datasets. In his professional role, he has undertaken the challenge of developing predictive models for diverse outcomes. His work includes predicting suicide behavior using medical data, estimating university rankings, and forecasting student retention rates. He employs advanced statistical methods, including penalized regression, model fusion, and the MCMC method, in his pursuit of accurate and reliable predictions. While he is proficient in various platforms and languages, including R, Python, Boyang continually strives to enhance his expertise and broaden his knowledge in various statistical models.
BS, Statistics, University of North Carolina- Chapel Hill
MS, Data Science, University of Wisconsin-Madison
