About Jiantong Wang
Download my CV here
Hi! I am Jiantong Wang, a Ph.D. candidate in the Department of Operations, Business Analytics, and Information Systems (OBAIS) at the University of Cincinnati, Lindner College of Business. My research focuses on developing novel methodologies and computational tools for analyzing complex data to address challenging and impactful questions in business and healthcare. Beyond research, I am deeply passionate about teaching and take pride in creating engaging and supportive learning environments that inspire students to apply analytical skills to solve real-world problems.
Teaching Interests
Business Analytics Methods, Data Mining, Optimization, Statistical Methods, Data Visualization, Time Series Analysis, Database Management
- Analytics Courses: Business Analytics, Optimization Methods, Forecast Analysis, Simulation Modeling and Methods, Data Visualization, Database Management
- Statistics Courses: Statistical Methods, Data Mining, Statistical Modeling, Time Series Methods, Big Data Analytics, Statistical Computing
- Programming/Software Courses: R, Python, C++, Shell, SAS, Spreadsheet Analysis
Course Websites
BANA4090 Forecasting and Risk Analysis
BANA7046 Data Mining
Awards
Outstanding Doctoral Student Teaching Awards, by Lindner College of Business, University of Cincinnati
Honorable Mention, Excellence in Teaching Award, by University of Cincinnati
Research Interests
High Dimensional Data Analysis, Machine Learning, Healthcare Analytics, Optimization, Human Genetics, Corporate Bankruptcy, Multivariate Analysis, Network Analysis
Working Paper
Wang J., Lian H., Yu Y., Zhang H.“Quantile Regression with Insight Fusion for Ultrahigh Dimensional Data with Application to Obesity,” targeting Journal of the American Statistical Association
Research in Progress
“A Comprehensive Examination for Machine Learning Methods in Corporation Bankruptcy Prediction,” with Yan Yu, targeting Review of Accounting Studies.
“Modeling Gene-Environment Interactions in Psychiatric Comorbidity: Generalized Multivariate Varying Coefficient Model,” with Tianhai Zu, Heng Lian, Yan Yu, targeting Journal of Multivariate Analysis.
“Identifying Important G×E for High Quantiles of BMI in UK Biobank Data,” with Yan Yu (Lindner REC awarded project).
“Shrinkage Bootstrap: A Novel Bootstrap Method for Random Dot Product Graph (RDPG),” with Yichen Qin.
“PriorEN: A Novel Elastic Net Model Incorporating Prior Insights for Ultra-high Dimensional Data” with Yan Yu.
Conference Prensentations
“Identifying Genetic Variants for Obesity Incorporating Prior Insights: Quantile Regression with Insight Fusion for Ultra-high Dimensional Data”, INFORMS Annual Meeting, Seattle, WA, Oct, 2024.
“A comprehensive examination for machine learning in corporate bankruptcy prediction”, Joint Statistical Meetings (JSM), Portland, OR, Aug, 2024.
“Identifying Genetic Variants for Obesity Incorporating Prior Insights: Quantile Regression with Insight Fusion for Ultra-high Dimensional Data”, New England Statistics Symposium (NESS), online, May, 2024.
“Penalized Quantile Regression Incorporating Prior Information for Ultra-high Dimensional Data”, Joint Statistical Meetings (JSM), Toronto, ON, Aug, 2023.
“Penalized Quantile Regression Incorporating Prior Information for Ultra-high Dimensional Data”, Bayesian, Fiducial & Frequentist Conference (BFF), Cincinnati, OH, May, 2023.
“Shrinkage Bootstrap: A Novel Bootstrap Method for Random Dot Product Graph (RDPG)”, INFORMS Annual Meeting, Indianapolis, IN, Oct, 2022.