Dr. Hsieh is currently an Assistant Professor of Management Sciences in the Department of Information Systems and Supply Chain Management. She received her Ph.D. degree in Statistics from the Stern School of Business, NYU in 2006. She has over eight years of applied research experience at diverse private organizations, including IBM Research, Morgan Stanley, Credit Suisse, and Activision. Her current research interests are applied statistics in supply chain management and predictive analytics.
Time Series Analysis, Financial Econometrics, Predictive Analytics
Statistics, Regression Model and Data Analysis, Forecasting Time Series Data
- Hsieh, M., Hurvich, C, & Deo, R. (2010). Long Memory in Intertrade Durations, Counts and Realized Volatility of NYSE Stocks. Journal of Statistical Planning and Inference, 140(12), 3715-3733.
- M. Hsieh, C. Hurvich and P. Soulier, “Asymptotics for Duration-Driven Long Memory Processes”, Journal of Econometrics, Vol. 141, December 2007, pp. 913-949.
- M. Hsieh, C. Hurvich and R. Deo, “Long Memory in Intertrade Durations, Counts and Realized Volatility of NYSE Stocks”, Journal of Statistical Planning and Interference, Vol. 140., Issue 12, December 2010, pp. 3715-3733.
- R. Deo, M. Hsieh, C. Hurvich, and P. Soulier, “Long Memory in Nonlinear Processes,” Dependence in Probability and Statistics, Lecture Notes in Statistics, Vol. 187, Springer (May 2006)
- Method and Apparatus for Workforce Demand Forecasting, US Patent 8015043
- Method and Structure for End-to-End Workforce Management, published application 2008/0167930 A1
- Method and Structure for Generic Architecture for Integrated End-to-End Workforce Management, published application US 2008/0167929 A1
Papers Under Review
- Hsieh, M., Giloni, A., & Hurvich, C. (2016). Impact of Exponential Smoothing on Inventory Cost in Supply Chain. Initial submission to Production and Operations Management.