The abundance of personal data that people share online provides an opportunity to study social phenomena at a large scale. It also enables the development of a new category of information technology products, ones that are powered by data and sophisticated user models. Data science products range from personalized recommendations to fostering healthy online communities, and they bring together advances in machine learning, causal inference, and big data technologies. In this talk, I will go over my work on using data science to study sharing incentives and recommendations, and also discuss the privacy implications of machine algorithms for social media users and businesses.
Bio: Elena Zheleva is an assistant professor in Computer Science at the University of Illinois at Chicago. Her research interests span data science, machine learning, causal inference, network science, and online privacy. She has presented her research at top-tier conferences, such as KDD, WSDM, and WWW, and she is the coauthor of the book 'Privacy in Social Networks.' Her experience includes building and managing a data science team at an e-commerce company, and working on initiatives at the intersection of public policy and data science at NSF. She obtained her Ph.D. in Computer Science from the University of Maryland College Park in 2011.