Hari Sundaram designs algorithms and builds computational systems that analyze collective behavior in large-scale online social networks. His research has received several awards from the IEEE and the ACM societies. He has made contributions to core problems in social network analysis: community discovery; detecting the onset of coordinated behavior; rapid detection of large-scale changes to network structure; the design of network sampling methods. His current research is motivated by the challenge: how can we persuade millions of people to adopt behaviors that would be beneficial to them? Example behaviors include: leading healthy lifestyles; reducing individual energy consumption and greater civic engagement. The widespread adoption of these behaviors would lead to large-scale societal benefits such as reduced healthcare costs, sustainability and a vibrant community. But, despite knowledge of benefits, many individuals do not adopt these behaviors in part due to time and resource constraints. To address this challenge, his research group is working on several interrelated research areas: analysis of social signals in large-scale networks (group formation, identifying influentials), mechanism design to incentivize behavior, using smartphones and developing lightweight wearable sensors to sense physical activity, and creating computational advertising frameworks for persuasion (how and when to target individuals, algorithmic advertisement synthesis).