*Names in bold indicate Presenter

Our objective is to forecast the growth of demographic cohorts of full time graduate students in public and private institutions, taking into account how the growth of each cohort influences the other, and the impact of funding sources. We use the *Survey of Graduate Students and Postdoctorates in Science and Engineering*[GSS] as data sources, and a machine learning approach to time series modeling (Darlington 1996) based on support vector regression (Shevade et al. 1999) to forecast dependent and independent variables in parallel. Our dependent variables describe rates of fulltime graduate students by race, gender and US citizenship; our independent variables include sources of funding (federal such as NIH, DOE, NSF, non-federal, and self-support). An evaluation of forecasts 15-year onward with Mean Absolute Percentage Error (MAPE) and Direction Accuracy (DAC) corroborates the robustness of the modeling approach (on average, MAPE is 6% and DAC 86%). We also simulate the impact of policy changes, such as reducing to a half the number of foreign students and cutting down federal funding by 25%, on growth rates of graduate students by race and gender, using overlay/intervention variables. The emerging paradigm provides a promising framework to analyze diversity in the scientific workforce.

**References**

- NAP-07 (2007)
*Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future*. The National Academies Press, Washington DC. - NAP-10 (2010) Expanding Underrepresented Minority Participation: America's Science and Technology Talent at the Crossroads. The National Academies Press, Washington DC.
- SDR (2001-2008) NSF Survey of Doctoral Recipients. 2001-2008 public files, http://sestat.nsf.gov/datadownload/.
- USCB-12 (2012) U.S. Census Bureau, Statistical Abstract of the United States: 2012, Table 6.
- GSS, http://www.nsf.gov/statistics/srvygradpostdoc.
- Richard B. Darlington (1996) A Regression Approach to Time Series Analysis, http://www.psych.cornell.edu/Darlington/series/series0.htm.
- S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999.