Roy Welsch

Roy Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a professor of statistics and data science at MIT’s Sloan School of Management. He is also a member of the Center for Statistics and Data Science at the MIT Institute for Data, Systems, and Society. He is currently involved in research on robust process control and experimental design, credit-scoring models and risk assessment, diagnostics for checking model and design assumptions, volatility modeling in financial markets, uncertainty quantification in plots and images, transparent multivariate models, and repurposing existing drugs or combinations of drugs to prevent or delay Alzheimer’s disease by creating synthetic clinical trials based on electronic health records. Welsch earned a BA in mathematics from Princeton University, and an MS and a PhD in mathematics from Stanford University.

Selected Publications

• Seth, M., Gerovitch, A., Welsch, R., Markuzon, N. (2019). The Univariate Flagging Algorithm (UFA): An Interpretable Approach for Predictive Modeling. PLOS One 14(10): e022316.
• Zing, F., Cambria, E., Welsch, R. (2019). Intelligent Asset Management. Springer, New York.
• Zhu, Z., Welsch, R. E. (2018). Robust Dependence Modeling for High-Dimensional Covariance Matrices with Financial Applications. The Annals of Applied Statistics. Vol. 12, No. 2 (2018): 1228-1249.
• Chaturvedi, I., Ong, Y.-S., Tsang, I. W., Welsch, R. E., Cambria, E. (2016). Learning Word Dependencies in Text by Means of a Deep Recurrent Belief Network. Knowledge Based Systems Vol. 108: 144–154.

Media

• December 19, 2017: OpenGovAsia.com, Coupling top-down and bottom-up approaches to Natural Language Processing.
• May 1, 2015: MIT News, Student chips in with new technique for cutting silicon wafers.