Title: Efficient Methods for Large-Scale Data Analysis Abstract: The Information Age is dominated by large data volumes, transfers, and real-time communications; therefore, there is a broad demand for scalable, dynamic, and low-complexity algorithm. Many systems involve a large number of interacting modules; it is natural to represent these interactions and transfers of information in terms of a network. As a case study, I examine the viral marketing problem in online social networks. I present efficient algorithms for the influence maximization problem with partial incentives on large-scale social networks, which involves the approximation of a non-submodular optimization problem. Bio: Alan Kuhnle is a Postdoctoral Fellow in the Computer & Information Science & Engineering Department at the University of Florida; he is partially supported by the Informatics Institute at the University of Florida. He received his Ph.D. in Computer Science at the University of Florida in 2018. His research interests include data mining/data science, machine learning, and bioinformatics. Recently, Kuhnle has served as Program Committee member for The WebConf (WWW) 2019. For his work on misinformation in social networks, he received Best Runner-Up Paper Award at ASONAM 2018.