Network Science of Human+Agent Teams

The U.S. Army funded Multidisciplinary University Research Initiative (MURI) project is studying human teams and human-agent teams in order to (a) build quantifiable informative models of teams as dynamical systems interacting over multiple networks, (b) analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance, and (c) break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams). 

Smart Farms and the Internet of Things

The UCSB SmartFarm project is a computer science led effort focused on the use of data science with “The Internet of Things” (IoT) to improve sustainable agriculture, water usage and land management. Using novel systems and data science applications developed in the RACE Lab in the Computer Science Department, researchers have improved the analysis techniques commonly used by agronomists to analyze soil surveys. They have also been investigating new, data science based approaches to frost prevention and differential irrigation as ways of both saving water and sustaining agricultural yields.

UCSB data science research also enables scientists and citizen scientists to automatically classify wildlife images gathered from ecological preserves for use my local and statewide land management agencies and environmental researchers. The “Where’s the Bear?” project combines new computer science and data science research with open source analysis software with IoT systems and public clouds to monitor wildlife in remote areas.

Responsible Machine Learning

Artificial intelligence (AI) is changing our world. The Center for Responsible Machine Learning reflects UC Santa Barbara's commitment to advancing cutting-edge research in AI, machine learning, natural language processing, and computer vision, with an emphasis on the societal impacts of these rapidly evolving technologies. We are particularly interested in addressing issues of fairness, bias, privacy, transparency, explainability, and accountability in the context of AI algorithms, and in understanding the wide range of ethical, policy, legal, and even energy-efficiency issues associated with machine-learning models. We envision the center becoming an indispensable locus of innovation, where bold leaders produce visionary software and revolutionary techniques that powerfully serve the greater good.