معرفی
Johan Ugander is an Associate Professor of Management Science & Engineering (MS&E) at Stanford University, affiliated with the School of Engineering. He is also a member of the Social Algorithms Lab (SOAL), the Institute for Computational & Mathematical Engineering (ICME), and the Center for Computational Social Science. Prior to Stanford, he held a postdoctoral position at Microsoft Research and was affiliated with Facebook's Data Science team. He received his Ph.D. in Applied Mathematics from Cornell University (2014), with additional degrees from the University of Cambridge, Lund University, and Deep Springs College.
His research focuses on algorithmic and statistical frameworks for analyzing social networks, causal inference, and large-scale data. Key areas include graph theory, machine learning, and network experimentation. He has developed methodologies for seed set expansion, causal inference under network interference, and scalable computational tools for social systems analysis.
Ugander teaches courses such as Networks (MS&E 135), Social Algorithms (MS&E 231), and Data Privacy & Ethics (MS&E 234). He has advised numerous Ph.D. students and postdocs in areas spanning computational social science, network science, and machine learning.
His work has been supported by NSF, ARO, Cisco, and the Hellman Foundation. Notable contributions include foundational studies on social contagion, graph partitioning, and the analysis of Facebook's social graph structure. He is on sabbatical at Yale University for 2024–25.
