David Schoenebeck

David Schoenebeck
Name	David Schoenebeck
Fields	Computer Science; Computational Social Science; Human-Computer Interaction; Machine Learning
Workplaces	University of Michigan; University of Washington; Microsoft Research; Yahoo! Research
Alma mater	University of Wisconsin–Madison; Carnegie Mellon University
Doctoral advisor	Michael Kearns

David Schoenebeck is a researcher in computer science known for work at the intersection of machine learning, human–computer interaction, and computational social science. He has developed algorithms and empirical methods that address problems in social networks, privacy, social media, and algorithmic fairness. His career spans academic appointments, industrial research labs, and collaborative projects with scholars across economics, sociology, and communication studies.

Early life and education

Schoenebeck completed undergraduate and graduate training with mentorship connected to institutions such as University of Wisconsin–Madison and Carnegie Mellon University, where he studied under advisors including Michael Kearns. During his formative years he engaged with research communities at venues like SIGCHI, NeurIPS, ICML, AAAI, and WWW, collaborating with scholars from Princeton University, Massachusetts Institute of Technology, Stanford University, and University of California, Berkeley. His education intersected with programs and initiatives funded or coordinated by organizations such as the National Science Foundation, DARPA, and Microsoft Research.

Academic career and research

Schoenebeck has held faculty and research positions at institutions including University of Michigan and visiting roles associated with University of Washington and industrial labs such as Microsoft Research and Yahoo! Research. His work appears across conferences and journals connected to ACM, IEEE, and interdisciplinary venues like Science Advances and Proceedings of the National Academy of Sciences. He collaborates with researchers affiliated with Cornell University, Columbia University, University of Pennsylvania, Harvard University, Yale University, and international centers including University of Oxford, University of Cambridge, ETH Zurich, and Max Planck Institute for Software Systems.

His research program integrates methods from graph theory communities tied to Graph Drawing and SODA with empirical techniques used by scholars at Pew Research Center, Knight Foundation, and policy units like Brookings Institution. He has issued invited talks at forums such as AAAS, ACM CHI, ICWSM, and panels organized by National Academies of Sciences, Engineering, and Medicine.

Contributions to computational social science

Schoenebeck is noted for contributions addressing privacy leakage and behavior on platforms including Facebook, Twitter, Instagram, Reddit, and LinkedIn. He developed models and experiments that draw on theories from behavioral economics research groups at University of Chicago and London School of Economics while employing algorithmic techniques aligned with differential privacy work from Cynthia Dwork and others at Harvard University and Microsoft Research. His studies examine information diffusion related to events like the 2016 United States presidential election, the 2018 midterm elections, and public health crises such as the COVID-19 pandemic, connecting findings to policy debates involving institutions like the Federal Trade Commission and European Commission.

Methodologically, Schoenebeck has combined computational models similar to those in network science from Albert-László Barabási's school with causal inference approaches used by researchers at Harvard School of Public Health and Stanford School of Medicine. His work on algorithmic accountability engages with scholars from Data & Society Research Institute and legal scholars at Yale Law School and Columbia Law School, contributing to conversations about transparency in systems developed by companies such as Google, Meta Platforms, Twitter, Inc., Amazon, and Apple Inc..

He has also advanced methods for studying online harassment and moderation, overlapping with projects at Mozilla Foundation, Electronic Frontier Foundation, and advocacy groups like ACLU and Center for Democracy & Technology. Cross-disciplinary collaborations include teams at Johns Hopkins University, University of California, San Diego, Georgia Institute of Technology, University of Illinois Urbana–Champaign, and Rutgers University.

Awards and honors

Schoenebeck's work has received recognition from bodies such as the National Science Foundation CAREER-related programs, best-paper awards at conferences like ACM CHI and ICWSM, and grants from agencies including NIH and foundations like the Andrew W. Mellon Foundation. He has been named in early-career distinguished lists maintained by organizations such as ACM and IEEE and invited to workshops convened by Pew Research Center and National Science Foundation panels addressing research strategy. His research collaborations have been supported by awards from the Gordon and Betty Moore Foundation, Simons Foundation, and corporate fellowships at Microsoft Research.

Selected publications

- Schoenebeck, D., Kearns, M., et al., publications in venues including NeurIPS, ICML, and AAAI addressing algorithmic game theory and privacy. - Papers on social media behavior published at ACM CHI, ICWSM, and WWW examining Facebook and Twitter dynamics. - Interdisciplinary articles coauthored with public-health researchers in journals such as Science Advances and PNAS on information spread during the COVID-19 pandemic. - Work on fairness and accountability cited in reports from Brookings Institution and Data & Society Research Institute; conference presentations at AAAI/ACM Conference on AI, Ethics, and Society. - Methodological contributions to causal inference and network experimentation appearing in Journal of the Royal Statistical Society-aligned venues and methodological workshops hosted by NeurIPS and ICML.

Category:Computer scientists Category:Computational social scientists