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Mahantesh Halappanavar, PhD

Chief Scientist and Group Leader, Data Sciences and Machine Intelligence Group

Mahantesh Halappanavar, PhD

Chief Scientist and Group Leader, Data Sciences and Machine Intelligence Group


The ability to analyze relationships—whether they be among people, proteins, atoms, or some other unique entities—can drive discovery. Computing advances are making it possible to build even more complex webs of information.

Mahantesh Halappanavar works on the graph algorithms that drive such discovery and analysis tasks, which can be applied to a wide range of problems. He has contributed to the development of graph theoretic models that, for example, can identify the most effective vaccine distribution strategy based on contact networks, for example. In other research, he and colleagues built an algorithm that identified cell populations within large datasets about 27 times faster than current methods. This type of cell analysis can aid understanding of how disease can lead to changes in specific cell populations.

As the principal investigator of ExaGraph, an applications co-design center funded by the Department of Energy’s Exascale Computing Project, Halappanavar is pushing graph analytics toward the next generation of supercomputers. He also co-leads Data-Driven Decision Control for Complex Systems (DnC2S), a collaborative project with Oak Ridge National Laboratory, University of Arizona, and University of California-Santa Barbara, to control and optimize complex systems by developing data-driven machine learning and artificial intelligence.

DnC2S aims to strengthen the reasoning capability of machines. “We might want an algorithm to control operations that a human cannot do efficiently, such as constant monitoring of a nuclear reactor or a large building,” Halappanavar said. “But you want that algorithm to be reliable, and you need to know why it is doing something.”

Halappanavar has authored over 100 technical publications for peer-reviewed journals, conferences, and workshops. He is a member of the Society for Industrial and Applied Mathematics (SIAM), and a senior member of the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronics Engineers (IEEE).

More Information

PNNL Staff Biography

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Applying Graph Algorithms of Key Importance to the Nation. July 2, 2021, Exascale Computing Project Podcast.

Maximizing a vaccine campaign by analyzing social interactions. December 2, 2020, GCN.

Novel Coronavirus Prompts Computer Sharing.” April 23, 2020, APS Physics.