Daniel Sussman, Institute Junior Faculty Fellow, to Give 11/1 Wed@Hariri/Meet Our Fellows Talk

3:00 PM – 4:30 PM on Wednesday, November 1, 2017
Refreshments & networking at 2:45 PM
Hariri Institute for Computing
111 Cummington Mall, Room 180

The Hariri Institute for Computing continues its 2017-2018 “Meet Our Fellows” series, which will showcase the Institute’s 2017 Junior Faculty Fellows and Graduate Student Fellows. Prior to Junior Faculty Fellow presentations, a Graduate Student Fellow will give a 5-minute preview of his or her current research.

Meet Our Fellows/Research Preview: Kate Mays
Hariri Graduate Fellow, Hariri Institute for Computing
PhD candidate, Emerging Media Studies (COM)
Mays’ research examines the effects of technological affordances on social relationships, with a specific focus on mobile dating applications.

Meet Our Fellows/Junior Faculty Fellow Presentation
Daniel Sussman
Junior Faculty Fellow, Hariri Institute for Computing
Assistant Professor, Mathematics & Statistics

Multiple Network Inference: From Joint Embeddings to Graph Matching

Abstract: Statistical theory, computational methods, and empirical evidence abound for the study of individual networks. However, extending these ideas to the multiple-network framework remains a relatively under-explored area. Individuals today interact with each other through numerous modalities including online social networks, telecommunications, face-to-face interactions, financial transactions, and the sharing and distribution of goods and services. Individually these networks may hide important activities that are only revealed when the networks are studied jointly. In this talk, we’ll explore statistical and computational methods to study multiple networks, including a tool to borrow strength across networks via joint embeddings and a tool to confront the challenges of entity resolution across networks via graph matching.

Bio: Daniel is an assistant professor in the Mathematics and Statistics Department. Prior to joining BU, Daniel served as a postdoctoral fellow at Harvard University for two years. He received his Ph.D. in applied math and statistics from Johns Hopkins University in 2014. Daniel develops theory and methods for studying network data. He has studied spectral methods and other tools to embed the nodes of a network in Euclidean space. He is also interested in tools to study multiple networks simultaneously, especially when the networks arise from disparate sources. Finally, he researches how to perform and analyze experiments on networked groups of individuals.