Harnessing big data to create usable, high-value information

Big data has enormous value — but only if the proper tools exist to process, organize, analyze and unlock the riches residing in these dense, ever-expanding troves. 

The volume and diversity of data generated on a daily basis is growing exponentially. Information gathered from computers, smartphones, satellites, sensors, microprocessors and other sources has the potential to reveal valuable and actionable insights.

In health care alone, analytics of big data could change every inch of the nation’s $3 trillion health care industry. The payoff would be enormous: better patient outcomes, greater transparency, and more accessible and affordable care. That’s why the research of Yi Chen, associate professor in both NJIT’s Martin Tuchman School of Management and College of Computing Sciences, is vital. Dr. Chen’s work is focused on harnessing social media and online health care forums to create patient-centered tools to help patients, caregivers and doctors while also enabling researchers to discover and generate new knowledge. With the sponsorship of The Leir Charitable Foundations, Dr. Chen is also bringing together top researchers, policymakers, and business and tech leaders. Her overarching goal is to galvanize action and identify the challenges, opportunities and future directions of big data as it relates to improving health care.

On another front, Associate Professor of Computer Science Chase Wu is working to tame the explosion of information and increase the productivity of scientists using big data. Currently, scientific applications typically generate terabytes of simulation or experimental data — all of which must be stored, managed and often transferred to different geographical locations for processing and analysis — overwhelming scientific computing and network infrastructures. With funding from the U.S. Department of Energy’s Office of Science, Dr. Wu is designing and developing easy-to-use computing and networking toolkits for the complex workflows in these high-performance environments.

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Yi Chen
Associate Professor, Martin Tuchman School of Management and College of Computing Sciences

Chase Qishi Wu
Associate Professor of Computer Science, College of Computing Sciences