Robert Clements is an assistant professor in the MS in Data Science program. His interests include making data science concepts more accessible to a general audience through visual methods, and combining data, code and models with art. Prior to joining USF he had a nearly ten-year career in industry, holding several positions throughout the San Francisco Bay Area as a data scientist and data science manager/director, working primarily in developing machine learning models in different domains. He received a PhD in Statistics from UCLA in 2011 and, before beginning his industry career, was a postdoctoral researcher at the German Research Center for Geosciences in Potsdam, Germany, where he studied statistical seismology.
- UCLA, PhD in Statistics, 2011
- UCLA, MS in Statistics, 2009
- Humboldt State University, BA in Mathematics, 2006
- Senior Director of Data Science, Optum
- Data Scientist & Manager, Walmart Labs
- Data Scientist, UnitedHealthcare
- Data Scientist, GE Digital
- Data Scientist, Verisk Analytics
- Clements RA, Schoenberg FP, and Schorlemmer D. 2011. Residual analysis methods for space-time point processes with applications to earthquake forecast models in California. Annals of Applied Statistics. 5 (4): 2549-2571.
- Clements RA, Schoenberg FP, and Veen A. 2012. Evaluation of space-time point process models using super-thinning. Environmetrics. 23: 606-616.
- Schneider M, Clements RA, Schorlemmer D, and Rhoades D. 2014. Likelihood- and Residual-Based Evaluation of Medium-Term Earthquake Forecast Models for California. Geophysical Journal International. 198 (3): 1307-1318.
- Gordon SJ, Clements RA, Schoenberg FP, Schorlemmer D. 2015. Voronoi residuals and other residual analyses applied to CSEP earthquake forecasts. Spatial Statistics. 14B: 133-150.
- Mak S, Clements RA, and Schorlemmer D. 2017. Empirical Evaluation of Hierarchical Ground Motion Models: Score Uncertainty and Model Weighting. Bulletin of the Seismological Society of America. 107(2): 949-965.