Data Science Analytics
Featured here are a series of case studies of research at the University of Rochester in the area of data science analytics.
In addition to researchers who represent a broad spectrum of traditional disciplines, Brendan Mort of the Center for Integrated Research Computing (CIRC) describes the center’s state-of-the-art, high-performance computing facilities and consulting services, which enable these projects and many others.
These projects are just a sample of data science research at the University of Rochester. Work in data science is continuing to grow as a high-priority area for expansion in all of the University’s colleges and schools.
Matthew Blackwell, [political science] uses data science analytics to understand political campaigns and other issues in American politics.
Daniel Gildea, [computer science] develops systems that translate between human languages by training on huge corpora of parallel texts.
Henry Kautz, [computer science] is data mining social media such as Twitter in order to identify global disease outbreaks in their earliest stages and track their spread.
Jiebo Luo, [computer science] works on image understanding, developing systems that can automatically label images, videos, and other kinds of multimedia.
Rajeev Raizada, [brain and cognitive sciences] uses pattern-based fMRI analysis in order to understand the way the brain encodes and processes information.
Huaxia Rui, [Simon School of Business] focuses on how business can make use of data from social media sites such as Twitter and Facebook to improve decision-making.
Vincent Silenzio, [psychiatry] works on suicide prevention among at-risk youth and explores the use of online social networks to gather data from people who are otherwise difficult to identify or survey.
Axel Wismueller, [imaging sciences and biomedical engineering] develops novel, intuitively intelligible computational visualization methods for the exploratory analysis of high-dimensional data from biomedical imaging.
Robert Strawderman, [biostatistics and computational biology] looks at the growing portfolio of research in large-scale medical statistics and how it is changing our approach to personalized health care.