Christoph Riedl, Lab Director

Christoph Riedl is Assistant Professor for Information Systems at the D’Amore McKim School of Business. He employs business analytics and data science to investigate research questions about group-decision making, network science, and social media, and develops novel computational approaches to study collective intelligence mechanisms.

Post-docs and Students

Stefano Balietti, Postdoctoral Research Associate

Stefano Balietti is a Postdoctoral Researcher at the Network Science Institute, and a Fellow at the Harvard Institute for Quantitative Social Sciences (IQSS). His research interests involve: incentives schemes for peer review systems, consensus formation and social influence -- in particular in epistemic communities, equality and efficiency in public-goods games, efficiency in coordination games, philosophy of science -- in particular Paul Feyerabend's body of work. His methodology aims at bringing together agent-based computer simulations and behavioral experiments. He is also an active developer, and he created a JavaScript platform for conducting real-time online behavioral experiments directly in the browser called nodeGame.

Michael Foley, Third Year PhD Student

Michael's broad research interests lie in the overlap between complex systems and the social sciences. In particular, he is interested in how rational local decisions and interactions can produce unintended and emergent system behavior. Michael has a B.S. and M.S. in Mathematics from the University of Vermont, where he did research in computational finance and agent based modeling. Currently, he is working with Christoph Riedl to research the effect of different communication networks on a group's ability to solve problems.

Brennan Klein, Second Year PhD Student

Brennan studies surprise in complex systems. He received his BA in Cognitive Science and Psychology from Swarthmore College in 2014, studying the relationship between perception, action, and cognition. Now, he is focused on understanding how complex systems are able to represent, predict, and intervene on their surroundings across a number of different scales—all in ways that minimize the surprisal experienced in the future. This approach is used to study a range of phenomena, from human decision making, to optimal experimental design, to causal emergence in networks.

Ewen (Yuxuan) Wang, Undergraduate Research Assistant

Ewen is a first-year computer science student at Northeastern University's College of Computer and Information Sciences. Creating computer and mobile games since middle school, he found a strong passion for CS and has published well-received apps on the Play Store. Previously, he interned at Harvard Medical School’s Medical Imaging Lab, where he developed software to format patient data for machine learning. He currently researches optimal experimental design through agent-based simulations and is an active developer of the nodeGame experimentation framework.

Lab Alumni

  • Sam Fraiberger (now Data Scientist at the World Bank and Visiting Scholar at NYU Computer Science Department)
  • Praveen Ningappa
  • Jake Moody
  • Christina Sirabella
  • Tina Lee