Chang Gu PhD
About Chang Gu PhD
Chang Gu, PhD, is the Director of Data Science at Ally, with extensive experience in predictive modeling, big-data analytics, and quantitative research.
Title
Chang Gu, PhD, is currently the Director of Data Science at Ally, a position he has held since 2021. His work is located in the Dallas/Fort Worth Area.
Previous Roles at Ally
Prior to his current role, Chang Gu worked at Ally as a Data Science Manager from 2015 to 2021. Over his six-year tenure in this role, he honed his skills in data science management and predictive analytics.
Experience at Hyundai Capital America
Before joining Ally, Chang Gu held the position of Manager of Predictive Modeling at Hyundai Capital America for one year, from 2014 to 2015. His work was centered in Irvine, CA, where he focused on developing predictive models using advanced analytics tools.
Academic Background at Vanderbilt University
Chang Gu earned his Doctor of Philosophy (Ph.D.) in Brain & Cognitive Sciences from Vanderbilt University. He was associated with the university from 2010 to 2014, where he also served as a Data Scientist and Project Manager.
Educational Experience at Peking University
Chang Gu received both his Master’s Degree in Applied Mathematics and his Bachelor's Degree in Mathematical Sciences from Peking University.
Technical Expertise
Chang Gu possesses expertise in several analytical software and programming languages, including SAS, SQL, Matlab, R, and Python. His technical skills are particularly strong in predictive modeling and time-series forecasting.
Research in Predictive Modeling Techniques
Chang Gu has conducted seven years of research in various predictive modeling techniques such as logistic regression, decision trees, and neural networks to examine behavioral propensity.
Experience in Big-Data Analytics
With four years of professional experience in mining business data, Chang Gu has performed big-data analytics and database marketing. He also has four years of quantitative research experience in social science, focusing on experimental design and hypothesis testing.