The admission decision process is an important operational management problem for many universities. Admission control processes may, however, differ among universities. In this paper, we focus on the problem at Korea Advanced Institute of Science and Technology (KAIST). We assume that individual applications are evaluated and ranked based on paper evaluations and (optional) interview results. We use the term “university admission decision” to mean determining the number of admission offers that will meet the target number of enrollments. The major complexity of an admission decision comes from the enrollment uncertainty of admitted applicants. In the method we propose in this paper, we use logistic regression with past data to estimate the enrollment probability of each applicant. We then model the admission decision problem as a Markov decision process from which we formulate optimal decision making. The proposed method outperformed human expert results in meeting the enrollment target for the validation data in 2014 and 2015. KAIST successfully used our method for its admission decisions in academic year 2016.