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.”
Despite recent advances, growth in the volume and complexity of data continues to outpace the development of new techniques needed to translate these data into cutting edge research. At the same time, the application of big data to new questions and disciplines requires novel approaches.
In its initial stages, the Data Science Initiative will include a new one-year Master’s degree in data science, expanded undergraduate course offerings, and the addition of ten new faculty members and researchers whose research and teaching will focus on fundamental methods of data science and their application to a variety of research questions.
The Data Science Initiative aligns with Brown’s commitment, as articulated in the University’s Building on Distinction strategic plan, to taking an integrative approach to developing solutions to complex challenges — an approach that bridges and unites multiple academic areas of research and study. Brown’s departments of mathematics, applied mathematics, computer science and biostatistics will serve as the initiative’s hub, but a key focus will be to create a campus-wide community in data science, engaging students and faculty in life and physical sciences, social sciences and the humanities.
Ultimately, the initiative aims to ensure that scholars across Brown’s disciplines become fluent with data in a way that encourages them to integrate data science into their teaching and research in novel and creative ways.
“Different types of data —genome sequences, data from social networks and medical records, to name just a few— are giving rise to entirely new frameworks and theories on how to extract meaning from data,” said Jeffrey Brock, chair of the Mathematics Department and director of the initiative. “We want to explore fundamentally new techniques and methods for eliciting new knowledge from data.”
In addition to more traditional research projects in the life, physical and social sciences, scholars from the Data Science Initiative will work with Brown’s Cogut Center for the Humanities to seek new connections across the cultural divide between the sciences and humanities and ways of using data in new scholarly contexts.
Partnerships with Brown’s Watson Institute for International and Public Affairs and the Center for the Study of Race and Ethnicity in America will investigate the societal and cultural impacts of data, including questions related to data access, privacy, security, equity and justice.
“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.”
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.
“Building outward from these core departments, Brown will engage with the foundational questions of the data revolution, becoming a lighthouse for methodological innovation in data science,” Brock said.
The management consulting firm McKinsey & Company estimates that by 2018, the U.S. will have a shortage of 1.5 million managers capable of using data analysis to make informed decisions. There will be an additional shortage of as many as 190,000 employees with deep data skills necessary to develop complex analyses and communicate findings through visual media.
To prepare students for the data-enabled economy, faculty in the Data Science Initiative will partner with departments across campus to create data science course sequences to promote data fluency in students studying in a variety of disciplines. New faculty added through the initiative will expand the course options already available at Brown. Current course offerings include two introductory courses —“Data Fluency for All” in Computer Science and “What’s the Big Deal with Data Science” in Applied Math— both designed to introduce the field to students without much experience with data science techniques.
The master’s program, which began recruiting its initial cohort this month, will offer a deeper dive into the methods applied by data scientists. In addition to a core curriculum focusing on foundational mathematical and computational techniques, an elective class will let students explore particular applications of their choice. A capstone project will help students apply what they’ve learned to real-world questions and problems.
“The program aims to provide students with the deep data fluency necessary for leadership in data-centric careers,” said Carsten Binnig, adjunct professor of computer science and director of the master’s program. “Courses will provide a fundamental understanding of the tools of data science that students can apply in a huge variety of careers, whether in business, health care delivery, academic research or something else.”
In both the educational and research mission of the Data Science Initiative, Brock said that collaboration across the disciplines will drive the initiative as an overarching theme.
“You never know when a technique applied to a data problem will be useful in another,” Brock said. “An approach used by one of our physicists sifting through the data produced by the Large Hadron Collider could be helpful to someone who is looking at data on how people behave in an economic market.
“We want the Data Science Initiative to be a place where those connections are made.”
For more information, please click the link that follows to contact Brown CS Communication Outreach Specialist Jesse C. Polhemus.