Rate My Professor Sherri Ni

Dr. Xuelei Sherry Ni is a Professor of Data Science and Statistics and Director of the School of Data Science and Analytics at Kennesaw State University, within the College of Computing and Software Engineering. She earned her B.S. in mathematics from Nanjing University in 2000, M.S. in statistics from the Georgia Institute of Technology in 2004, and Ph.D. from the Georgia Institute of Technology in 2006. Previously, she served as Professor of Statistics and Interim Chair of the Department of Statistics and Analytical Sciences at Kennesaw State University. Dr. Ni specializes in data mining and artificial intelligence, with research focused on developing innovative data-driven solutions and machine learning methodologies to address complex problems across various domains, including peer-to-peer lending, risk modeling, predictive analytics, credit scoring, and churn prediction. Her work emphasizes supervised learning, classification, pattern recognition, feature selection, and neural networks.

Dr. Ni has authored numerous publications in these areas, including 'A Bivariate Model for Correlated and Mixed Outcomes: A Case Study on the Simultaneous Prediction of Credit Risk and Profitability of Peer-to-Peer (P2P) Loans' (2025), 'A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) Lending' (2023), 'Towards Profitability: A Profit-Sensitive Multinomial Logistic Regression for Credit Scoring in Peer-to-Peer Lending' (2022), 'Measuring Customer Similarity and Identifying Cross-Selling Products by Community Detection' (2020), 'Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor' (2020), and 'Developing and Improving Risk Models using Machine-learning Based Algorithms' (2019). She has secured funding as principal investigator for projects such as 'Sample and Extrapolation Validation' ($37,500 from Myers and Stauffer LC, 2022-2024, renewed to 2025) and 'Customer Level and Product Level Churn Analysis' ($5,000 from Southern Company, 2023). Under her guidance, student teams have achieved top honors in national competitions like the SAS Student Symposium. Her research has garnered over 400 citations, contributing significantly to advancements in machine learning applications for financial risk and analytics.