in many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type i error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. to address this need, the neyman-pearson (np) classification paradigm is a natural choice; it minimizes type ii error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type i error. although the np paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. common practices that directly limit the empirical type i error to no more than alpha do not satisfy the type i error control objective because the resulting classifiers are still likely to have type i errors much larger than alpha. this talk introduces the speaker's work on np classification algorithms and their applications and raises current challenges under the np paradigm.