Yingfei Wang will present her Pre-FPO on Tuesday, January 10, 2017 at 2pm in CS 402. The members of her committee are: Warren Powell (adviser); Bernard Chazelle and Mengdi Wang (readers); Szymon Rusinkiewicz and Han Liu (nonreaders). Everyone is invited to attend her talk. Abstract follows below. We consider the problem of sequentially making decisions under uncertainty, exploring the ways where efficient information collection influences and improves decision-making strategies. My thesis work provides a comprehensive set of techniques that span from designing efficient optimal learning algorithms in parallel computing environments, to making decisions under parametric belief models which introduce additional computational hurdles, to finite-time and asymptotic guarantees, with an emphasis on how efficient information collection can expand access, decrease costs and improve quality in health care. Specifically, (1)we offer a new perspective of interpreting ranking and selection problems as adaptive stochastic multi-set maximization problems and deriving the first finite-time bound of the knowledge-gradient. In addition, we introduce the concept of prior-optimality and provide another insight into the performance of the knowledge gradient policy based on the submodular assumption on the value of information. (2) Driven by needs among materials science society, we developed a Nested-Batch-KG policy for sequential experiments when experiments can be conducted in parallel and/or there are multiple tunable parameters which are decided at different stages in the process. (3) Motivated by personalized medicine, we develop knowledge-gradient type policies under Bayesian generalized linear models. Finite time bound and asymptotic convergence are proved. We extend the knowledge gradient policy to the contextual bandit settings. A study on how to reduce health care costs on a real world knee replacement dataset shows that we can significantly improve the success rates. Since the high sparsity and the relatively small number of patients makes leaning more difficult, with the adaptation of an online boosting framework, we further develop hierarchical knowledge-gradient policies to sequentially make decisions in high-dimensional settings while balancing the contribution of each level of feature aggregation.