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578 Personal reflections on the COPSS Presidents’ Awardfinding meaningful relations among them. Recall Einstein’s commentthat, for science ‘truly creative principle resides in mathematics.’ Thecreativity of which Einstein speaks resides in the human mind. Thereappears to be an underlying assumption to data mining that the mindis inadequate when it comes to perceiving salient relations among phenomenaand that machine-based pattern searching will do a better job.This is not a debate between which can grope faster, the mind or themachine, for surely the latter can grope much faster. The debate isbetween the efficacy of mind in its creative synthesizing capacity andpattern searching, whether by the mind or the machine.”What success I have had comes from continuing to try to find researchproblems by working on applications and finding important/interesting appliedproblems that cannot be solved with existing methodology. I am spendingmuch of my time these days working on developing methods for dietarypatterns research, since nutritional epidemiologists have found that dietarypatterns are important predictors of cancer. I use every tool I have, and engagemany statistical colleagues to help solve the problems.ReferencesCarroll, R.J. (1978). On almost sure expansion for M-estimates. The Annalsof Statistics, 6:314–318.Carroll, R.J. (1982). Adapting for heteroscedasticity in linear models. TheAnnals of Statistics, 10:1124–1233.Carroll, R.J. (2003). Variances are not always nuisance parameters: The 2002R.A. Fisher lecture. Biometrics, 59:211–220.Carroll, R.J. (2014). Estimating the distribution of dietary consumption patterns.Statistical Science, 29:inpress.Carroll, R.J and Ruppert, D. (1980). Trimmed least squares estimation in thelinear model. Journal of the American Statistical Association,75:828–838.Carroll, R.J. and Ruppert, D. (1981). Prediction and the power transformationfamily. Biometrika, 68:609–616.Carroll, R.J. and Ruppert, D. (1982). A comparison between maximum likelihoodand generalized least squares in a heteroscedastic linear model.Journal of the American Statistical Association, 77:878–882.Carroll, R.J. and Ruppert, D. (1984). Power transformations when fittingtheoretical models to data. Journal of the American Statistical Association,79:321–328.