Building theoretical and practical tools to support responsible and reliable machine learning in social context.
Our research broadly revolves around theoretical and practical aspects of machine learning with a focus on social questions. We study machine learning as part of a broader sociotechnical ecosystem, explore the interaction of human populations with algorithmic systems and incorporate these insights into the fundamentals of how we design and study learning systems. Such systems can range from small-scale decision-support systems, to complex industry-scale machine learning applications, recommender systems, and digital platform markets.
Specific research areas of interest include interactive learning and optimization in dynamic environments, economic incentives and strategic behavior, the role of algorithmic decision making in digital economies and labor markets, the role of AI in social science research, as well as connections to law and policy.
Interested in joining or visiting the group?
If you are a looking for a PhD position, please apply through one of the following PhD Programs: ELLIS, CLS IMPRS-IS. If you have already graduated, please get in touch with me via email, I am actively looking for a motivated postdoc to join my group. Beyond this, I am always excited to host PhD students for research internships, and colleagues for a visit.