Brown Launches New Data Science Initiative Expanding Brown Executive Master Students Access to Cutting-Edge Research and its Powerful Implications for Business
Brown University has launched a Data Science Initiative to catalyze new research programs to address some of the world’s most complex challenges and provide students with innovative educational opportunities relating to “big data.” The initiative builds on established strengths in mathematical and computational sciences and a long history of data-related research across its core academic departments.
“From deciphering disease and improving the delivery of health care, to modeling climate change and evaluating public policies, Brown faculty are already on the cutting edge of the big data revolution,” said Brown President Christina Paxson. “The Data Science Initiative will build on that tradition and unearth new methods for using big data to solve big problems.”
The launch of this new center offers a wealth of new opportunities for Brown Executive Master students who seek to advance business with his powerful news analytical capacity and increase their expertise in this area.
“As the use of big data expands in commerce, public policy and in our everyday lives, it presents new challenges that cut across disciplinary boundaries,” said Brown Provost Richard M. Locke. “Brown’s Open Curriculum and collaborative research ethos put us in a unique position to help chart the future of the data-enabled society.”
An Initiative that Builds on Brown’s Distinction:Each new research program arising from the initiative will build upon a history and tradition of data-related research in the initiative’s core departments.
In the mid 1970s, a distinguished group of Brown faculty formed the Pattern Theory Group in Applied Mathematics. That team’s work in the early stages of image processing, computer vision, the theory of artificial neural networks and other areas established foundational data manipulation techniques widely used today.
Computer scientists at Brown are developing new algorithms and machine learning techniques for automated analysis of large datasets that may include text, audio, video and other types of information. Scholars are also creating new types of systems for manually searching, manipulating and visualizing data. Roboticists are using crowdsourcing and other big data techniques to increase the capabilities of robotic technologies.
The Department of Mathematics has research strengths in topology, geometry and graph theory, areas of pure mathematics that have found new application in data science. These techniques use the “shape” of datasets to identify clusters indicative of, for example, hubs in a social network or subtypes of a particular disease. These strengths complement those in harmonic analysis and cryptography, already central areas of data-related expertise.
Biostatistics faculty have leveraged data to create better screening protocols for lung cancer and public health strategies for preventing HIV spread. Researchers help to process genomic data to look for the mutations that drive cancer, as well as leveraging various datasets for precise and personalized treatment of individual patients.
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