Dr. Mallikarjun (Arjun) Shankar is the Director of the National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory. Before taking on the Division Director role in 2024, Shankar served as the Section Head for the Advanced Technologies Section (ATS) in the NCCS and as the director of the Compute and Data Environment for Science (CADES) at ORNL. He is a distinguished R&D staff scientist.

Dr. Shankar received his B.Tech. from the Indian Institute of Technology, Mumbai, and his M.S. and Ph.D. in computer science from the University of Illinois, Urbana. After his doctoral research he worked in industry designing and building next-generation content distribution infrastructures. His research in the national laboratory setting has involved designing large-scale data analysis and modeling systems, sensor networking systems, energy grid monitoring and control frameworks, and deploying middleware to overlay data, computation, and control across systems and infrastructure. His sponsored R&D project outputs have several active users in the federal government as well as in the commercial sector. His research has resulted in over seventy peer-reviewed publications including those that address jointly modeling and simulating systems coupled with observational data, incorporating policy constraints, and creating scalable cross-facility data infrastructure.

Dr. Shankar served on the DOE ASCAC subcommittee on Scientific and Technical Information. He is a member of the AAAS, a Senior Member of the ACM, and a Senior Member of the IEEE.


University of Illinois
Computer Science
Doctor of Philosophy (Ph.D.)
University of Illinois
Computer Science
Master of Science (M.S.)
Indian Institute of Technology
Computer Science and Engineering
Bachelor of Science (B.S.)

R&D Activities Contributions

CADES – Compute and Data Environment for Science - CADES is an ORNL facility to support R&D staff’s scalable computing and data analytics needs—making a research computing toolkit available to every research scientist. CADES…

Grid Modernization – Laboratory Consortium - The Grid Architecture project objectives are to provide a set of architectural depictions, tools, and skills to the utility industry and its extended stakeholders to…

Scalable Data Services Lifecycle and Orchestration - The Advanced Data and Workflow group brings a holistic view to scalable services that span the data lifecycle including how our users ingest, create, analyze,…

DataFed – A Federated Scientific Data Management System - DataFed is a federated, big-data storage, collaboration, and full-life-cycle management system for computational science and/or data analytics within distributed high-performance computing (HPC) and/or cloud-computing environments.…


2017 — ORNL Significant Event Award – EAGLE-I Deployment

2016 — ORNL Significant Event Award – BEAM System Workflow

2016 — DOE Secretary’s Appreciation Award, Healthcare Simulation

2009 — UT-Battelle Excellence in Science and Technology Award


M. Shankar, S. Somnath, S. Alam, D. Feichtinger, L. Sala, J. Wells, “Policy Considerations when Federating Facilities for Experimental and Observational Data Analysis,” Handbook on Big Data and Machine Learning in the Physical Sciences, Kalinin, Foster, eds., ISBN: 978-981-120-444-9
D. E. Womble, M. Shankar, W. Joubert, J. T. Johnston, J. C. Wells and J. A. Nichols, “Early experiences on Summit: Data analytics and AI applications,” IBM Journal of Research and Development, vol. 63, no. 6, pp. 2:1-2:9, 1 Nov.-Dec. 2019. doi: 10.1147/JRD.2019.2944146
Fagnan, Kjiersten, Nashed, Youssef, Perdue, Gabriel, Ratner, Daniel, Shankar, Arjun, and Yoo, Shinjae. "Data and Models: A Framework for Advancing AI in Science". United States. doi:10.2172/1579323.
Shankar, Mallikarjun, and Lancon, Eric, "Background and Roadmap for a Distributed Computing and Data Ecosystem". United States. doi:10.2172/1528707.
Yin, Junqi, Gahlot, Shubhankar, Laanait, Nouamane, Maheshwari, Ketan, Morrison, Jack, Dash, Sajal, and Shankar, Mallikarjun. "Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer". United States. doi:10.1109/DLS49591.2019.00016.