Cross-cutting Areas and Systems

CyberInfrastructure (Cloud, Computation, Networks)

CI, human and machine, is where ideas and fundamentals of data science get implemented in practical systems used by scientific communities to derive knowledge from data.This is an integration heavy area that could benefit/relate to many others.

Telecommunication Networks and Wireless Systems

Traditional approaches to traffic engineering and network deployments rely on generic modelling assumptions and rule of thumb over provisioning. Future generation systems, such as 5G systems, aspire to network vastly larger variety of devices to support highly diverse applications. The design and operation of these expensive, complex interconnected systems will be increasingly data driven and can benefit from advances in machine learning algorithms. Our goals in this regard include (1) creation of datasets to capture city scale data traffic and mobility patterns and (2) algorithms to infer numerous measures of value to network designers and operators as well as multiple disciplines, including public health, mental health, environment, transportation and energy usage.

Data-Driven System Design

We focus on the use of data science to reduce the difficulty and cost of complex system design processes that today require thousands of engineers and years of schedule. Goals include (1) modeling and prediction of design tool behaviors and outcomes, (2) discovery of appropriate optimization objectives to be applied at given stages of a design process, and (3) enabling accurate model-guided exploration of extremely large design (and design process) solution spaces.