Daniel Kojis is a rising senior at the University of Wisconsin-Madison double majoring in Statistics and Political Science. As an Amgen Scholar, he is working in the Department of Biostatistics with Dr. Christina Ramirez as they conduct machine learning research. Machine learning is widely used in medical settings for both prediction and discovering relevant factors. Their research focuses on extending a current machine learning method, random forests, to better incorporate longitudinal data and handle a high number of correlated variables. They expect to develop a new algorithm that will allow for better variable selection and prediction. Among other practical applications, this method could be used to predict if an individual will develop a disease, to determine the contributing factors of surgery outcomes, or to predict drug responses on medical patients. Daniel would like to thank the Amgen foundation and his mentor Christina Ramirez for supporting his growth as a researcher.