Shantenu Jha named Chair of Brookhaven Lab’s Center for Data-Driven Discovery

Jha holds a joint appointment with Rutgers University, where he is an associate professor in the Department of Electrical and Computer Engineering and principal investigator of the Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL).

Shantenu Jha

Computational scientist Shantenu Jha has been named the inaugural chair of the Center for Data-Driven Discovery (C3D) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, effective October 1. Part of the Computational Science Initiative (CSI), C3D is driving the integration of domain, computational, and data science expertise across Brookhaven Lab’s science programs and facilities, with the goal of accelerating and expanding scientific discovery. Outside the Lab, C3D is serving as a focal point for the recruitment of future data scientists and collaboration with other institutions.

Jha holds a joint appointment with Rutgers University, where he is an associate professor in the Department of Electrical and Computer Engineering and principal investigator of the Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL). He also leads a project called RADICAL-Cybertools, which are a suite of building blocks enabling the middleware (software layer between the computing platform and application programs) that supports large-scale science and engineering applications.

In his new role, Jha will work with domain science researchers, computational scientists, applied mathematicians, computer scientists and engineers. Together, they will develop, deploy, and operate novel solutions for data management, analysis, and interpretation that accelerate discovery in science and industry and enhance national security. These solutions include methods, tools, and services—such as machine-learning algorithms, programming models, visual analytics techniques, and data-sharing platforms. Initially, his team will focus on scalable software systems; distributed computing systems, applications, and middleware; and extreme-scale computing for health care and precision medicine. Partnerships with other national laboratories, colleges and universities, research institutions, and industry will play a critical role in these efforts.

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