Network Science of Teams

The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes.

However, it is not yet understood how patterns of interactions and relationships among team members (i.e. team networks) impact performance. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon. The goals of this project are to build quantifiable informative models of teams as dynamical systems interacting over multiple networks; analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance; and break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams), and advancing social science theories of team performance.

Selected Publications

Affiliated People

Research interests: 

Network Science, Social and Economic Networks, Algorithms, Data Mining, Systems and Control.


Victor graduated with a PhD from UCSB's Department of Computer Science in June, 2018, having worked on network processes with an emphasis on social and economic networks. In July, 2018, he joined the University of Pennsylvania's Warren Center for Network and Data Sciences as a Postdoctoral Fellow, where he worked until mid-2020, prior to joining as a Research Scientist. Before joining Dynamo Lab, Victor worked in scientific computing, and spent several years as a software engineer in industry. He also holds a MSc and a BSc degrees in Applied Mathematics and Computer Science.
Research interests: 

Applied Machine Learning, Complex Network Analysis, Convex Optimization, Multi-Agent Systems, Natural Language Processing

Omid received a B.Sc in Computer Engineering in 2011 and M.Sc. in Artificial Intelligence in 2014 from Sharif University of Technology, Tehran, Iran. Prior to joining Dynamo lab in 2015, he spent few years as a software engineer in industry. He has a background in complex networks, analysis of financial data and applied machine learning.

Research interests: 

Machine Learning, Deep Learning, Network Science


Aneesha received her B.E in Computer Engineering in 2017 from the Pune Institute of Computer Technology in India and joined UCSB in Fall 2018 to pursue master's in Computer Science. Prior to joining UCSB, she worked as a Software Engineer in the industry for a year. She joined Dynamo Lab in 2019

Research interests: 

Machine learning, data mining and network science. Specifically, clustering, semi-supervised learning, classification, relational learning, and causal reasoning.

Wei is a postdoctoral researcher with the DYNAMO lab. He received his Dr.rer.nat (PhD) degree in Computer Science from Lugwig-Maximilians University of Munich in 2018. Before joining the DYNAMO lab, he worked as a researcher in the Department of AI Platform, Tencent Inc. China.