COMPUTER SCIENCE
MACHINE LEARNING F
rom data-intensive applications that include voice, image, and facial recognition, to using deep neural networks and natural-language processing software to root out hate speech and democratize computing, to developing software that enables machine-learning (ML) models to operate across hardware platforms, UCSB computer-science engineers are at the forefront of this AIintensive moment.
PUSHING GROUP DYNAMICS Team projects are rife with group dynamics, which can affect outcomes and determine a group’s success. In some of his work, computer science professor Ambuj Singh uses machine learning to model the cascading events occurring in what he refers to as “structured information networks,” like those formed when humans (and AI agents) work together in teams. “We’re trying to understand how information evolves and cascades over these networks to arrive at a certain outcome,” he says. That involves developing machine-learning models that can predict such things as how the group will perform based on how they 18 Fall 2019
are interconnected and how they reach a decision on the task at hand. If a group is trying to make a distinction, Singh wants to know, “Is there some information related to the structure and how the individuals are connected and who is talking to whom that allows me to infer something at the higher level about the entire system, such as whether the group sentiment has changed, or if the group will achieve a good outcome? Or, can I predict that in the future, this structure will evolve into some other structure? In other words, can I model how this group is going to evolve over time and determine the right intervention?”