The accelerated growth of Big Data has created enormous amount of information at the macro level for knowledge discovery. But at the micro level, one can only expect a handful of observations in most individual users. This hinders the exploration of subtle patterns and heterogeneities among distinct users for improving the utility of Big Data analytics at a per-user basis. The objective of this project is to develop a set of algorithmic solutions to perform online learning in a collaborative fashion, where personalized learning solutions actively interact with users for feedback acquisition and collaborate with each other to learn from incomplete and noisy input. This project amplifies the utility of statistical learning in many important fields, such as healthcare, business intelligence, crowdsourcing, and cyber physical systems, where automated decision models are built on diverse, noisy and heterogeneous supervision. The research activities will be incorporated into teaching materials for student training and education in the areas of information retrieval, machine learning and data mining. This project consists of three synergistic research thrusts. First, it develops a family of contextual bandit algorithms to perform collaborative online learning over networked users. Dependency among users is estimated and exploited to collaboratively update the individualized bandit parameters. Second, it develops principled solutions to optimize task-specific and general loss functions for online learning, which enables the collaborative learning solutions reach more important real-world applications, such as information retrieval and user behavior modeling. Third, it models and differentiates the reliability of the sources of feedback to optimize the overall online learning effectiveness, which is especially important in the applications such as health informatics, crowdsourcing and cyber physical systems. Expected outcomes of the project include: 1) open source implementations for the developed online learning solutions; and 2) evaluation corpora that will enable researchers to conduct follow-up research in related domains.