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: 

Applied Machine Learning, Complex Network Analysis, Non-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.

Photo of Zexi Huang.
Research interests: 

Network Mining, Representation Learning, Graph Signal Processing, Transfer Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. During his bachelor, he also worked as a visiting research assistant at Nanyang Technological University, Singapore.

Research interests: 

computational methods for group dynamics in teams and organizations, decision theory for groups and inviduals, and methods of online learning & optimization.

Alex received his B.Sc. Computer Science & Engineering with a Minor in Mathematics from University of Southern Calfornia in 2015.

Research interests: 

Machine Learning, Network Science.

Mert received a B.Sc in Computer Science and Engineering in 2018 from Sabanci University, Istanbul. He joined Dynamo lab in 2018 as a Ph.D. student. and he is currently working on an event detection in dynamic graphs. He previously worked on graphs and differential privacy in his bachelor.

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.