Title: After Maximum Likelihood, What? Some Insights on Handling Outliers in Data Mining and Regression Abstract: Maximum likelihood estimation is universal but sensitive to model misspecification and outliers. In this talk, I describe an alternative approach to robust estimation. Robust estimation provides a powerful solution to practical problems in data mining. Simple tasks such as data cleaning may be prohibitively expensive with large datasets. These techniques can also handle the difficult situation where a dataset contains large clusters of outliers. In our approach, maximum likelihood is replaced by a data-based minimum-distance criterion. These ideas have application in regression as well. I am interested not only in the case of outlier-contaminated regression but also in the case of mixtures of regressions, with outliers. Examples of our approach will be given.