We are interconnected: online social networks when we communicate with each other, gene and protein interaction networks within us, traffic networks when we drive, trading and economic networks when we purchase, and the omnipresence of cell phones! These interconnections often depend on and result in the generation of large amounts of data. Preparing the first generation of professionals capable of analyzing, controlling, and modeling such empirical networks with large datasets in order to understand their fundamental unifying principles, with the ability to apply these principles to the design of more efficient and robust networks is the central theme of this IGERT.
The scientific world has experienced two significant paradigm shifts in recent years. The first involves the adoption of ‘Big Data’-driven discovery in diverse disciplines, ranging from biology and engineering to social sciences and psychology. The second is the clear understanding that the study of networks should not be isolated from the study of the processes that are connected by these networks and the large amounts of data derived and generated by them. Until recently, networks and data management were studied, modeled, and developed in isolation. Science and engineering have now embraced a systems approach that captures the fact that the interconnections between individual units, the very network itself, affect the behavior of a system much more than the individual components do. Spurred by this system-level view, Network Science has emerged as a scientific discipline that examines commonalities across diverse physical or engineered networks such as information networks, biological networks, and social networks.
There is an increasing need for trained scientists that can design control strategies for large networks, validate data-driven hypotheses, predict the dynamics of information cascades, design algorithms that operate at large scales, and understand the governing laws needed to make such networks robust.
Our IGERT program establishes an interdisciplinary graduate program in Network Science involving faculty and graduate students in seven departments: Computer Science (CS), Communication, Ecology, Evolution & Marine Biology (EEMB), Electrical & Computer Engineering (ECE), Geography, Mechanical Engineering (ME), and Sociology. The emphasis areas include computational methods that advance data-enabled science and engineering (scalable algorithms and Cyberinfrastructure), dynamics and control, social networks, and biological networks.
The program prepares students to engineer and control large networks, measure and predict the dynamics of networks, design algorithms to operate at scales of millions and billions of entities, make such networks robust, and develop new scientific hypotheses and principles about networks. UCSB has the experience in relevant interdisciplinary programs to create a cohort of PhD students who understand Network Science not from a single viewpoint, but from a unified perspective that is essential for continued success and career growth in the increasingly network-centric world. There is growing demand for such a trained interdisciplinary workforce from multiple domains of science, commerce, and national security, e.g., analysis of gene networks to find new therapies, intervention strategies in social networks to counter the spread of misinformation, and discovery of clandestine terrorism activity.
We are especially interested in trainees who can contribute to the diversity and excellence of the academic community through research, teaching, and service. We have an ambitious plan for recruiting, retaining, mentoring and graduating a diverse community of scientists.
Funding provided by the National Science Foundation (grant# DGE-1258507) and the University of California at Santa Barbara.