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Dan Jacobson On ORNL's Algorithms for Climate-Resilient Crops


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ORNL's Daniel A. Jacobson

"We've developed a new genomic selection algorithm that's driven by emerging machine learning methods collectively called 'explainable AI,'" says ORNL's Dan Jacobson.

Dan Jacobson, a research and development staff member in the Biosciences Division at the U.S. Department of Energy's Oak Ridge National Laboratory, has a few ideas on how artificial intelligence might impact agriculture, the food industry, and the field of bioengineering.

For the past five years, Jacobson and his team have studied plants to understand the genetic variables and patterns that make them adaptable to changing environments and climates. As a computational biologist, Jacobson uses some of the world's most powerful supercomputers for his work—including the recently decommissioned Cray XK7 Titan, and the world's most powerful and smartest supercomputer for open science, the IBM AC922 Summit supercomputer, both located at the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility at ORNL.

Last year, Jacobson and his team won the 2018 ACM Gordon Bell Prize after using a special computing technique known as "mixed precision" on Summit to become the first group to reach exascale speed—approximately a quintillion calculations per second.

In a Q&A, Jacobson talks about his team's work on a genomic selection algorithm, his vision for the future of environmental genomics, and the space where simulation meets AI.

From Oak Ridge National Laboratory
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