Science in Parallel: A Computational Science Podcast
Computers and science are intertwined – and not just as tools that help humans connect and collaborate. With computers, scientists model the earth’s climate, design alternative energy strategies and simulate exploding stars. From laptops to the world’s fastest supercomputers, software innovations and artificial intelligence are reshaping how we interact with mounds of data from healthcare to high-energy physics and how we solve critical problems.
Computational science brings together mathematics, computer science and hardware and science expertise to take on these challenges. In this podcast, you’ll meet the scientists doing this work, learn more about their research and gain insights into the workings of this dynamic field.
In this first podcast season, we’ll focus on the 30th anniversary of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program. All our guests will be fellows or alumni of the program. We’ll discuss a lot of science, including alternative energy, artificial intelligence and climate change, two-dimensional materials and hot, dense electrons. But we’ll also discuss the life experiences that shaped these scientists’ career paths and how they are influencing their fields and mentoring others.
Science in Parallel is produced by the Krell Institute. Our theme music was composed by Steve O’Reilly.
About the Host
Sarah Webb has worked as a science writer, editor and communicator for more than 15 years, writing for Nature, Science, Chemical & Engineering News, The Scientist and many other publications. She logged countless hours in the chemistry lab while completing a Ph.D. at Indiana University, but her favorite course was in computational biochemistry. Sarah is Krell’s Associate Science Media Editor.
Jeff Hittinger of Lawrence Livermore National Laboratory embodies the term scientist-chimera. He talks about the many scientific hats he’s worn simultaneously – computer scientist, applied mathematician and physicist. As director for the Center for Applied Computing (CASC) and as co-principal investigator for the DOE CSGF, he wears many more. He talks about scientific success, leadership and the tricks he’s cultivated for communicating science to broader audiences through the Livermore Ambassador Lecture series.
Jeff was a DOE CSGF recipient from 1996 to 2000 while earning his Ph.D. in aerospace engineering and scientific computing at the University of Michigan. He was one of the first recipients of the Frederick A. Howes Scholar Award and received the 2021 James Corones Award in Leadership, Community Building and Communication.
One of today’s hottest areas of computational research could help build better solutions for one of global society’s steepest challenges. Three early career computational scientists – Priya Donti, Kelly Kochanski and Ben Toms – talk about AI’s potential for understanding and predicting climate shifts, supporting strategies for incorporating renewable energy, and engineering other approaches that reduce carbon emissions. They also describe how AI can be misused or can perpetuate existing biases.
Working at this important research interface requires broad knowledge in areas such as climate science, public policy and engineering coupled with computational science and mathematics expertise. These early career researchers talk about their approaches to bridging this gap and offer their advice on how to become a scientific integrator.
Priya Donti is a Ph.D. student at Carnegie Mellon University, pursuing a dual degree in public policy and computer science, and a 4th year DOE CSGF recipient. She is also a co-founder and chair of the volunteer organization, Climate Change AI, which provides resources and a community for researchers interested in applying artificial intelligence to climate challenges. Priya was named to MIT Technology Review’s 2021 list of Innovators Under 35. Read more about Priya and her work in the 2021 issue of DEIXIS.
Kelly Kochanski completed a Ph.D. in geological sciences at the University of Colorado, Boulder in 2020 and works as a senior data scientist in climate analytics at McKinsey & Company. Kelly was a DOE CSGF recipient from 2016 to 2020, and her graduate research was featured in the DEIXIS 2020. She also is profiled in the 2021 issue as one of this year’s recipients of the Frederick A. Howes Scholar Award.
Ben Toms also finished his Ph.D. last year at Colorado State University studying atmospheric science and is a 4th year DOE CSGF recipient. He has founded a company, Intersphere, that provides weather and climate forecasts up to a decade into the future.
From the episode:
- Kelly and Priya contributed to the review article: Tackling Climate Change with Machine Learning, which was published on the arXiv preprint server in 2019.
- In the discussion about interpretable AI, Priya mentioned an article by Cynthia Rudin: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.
- Ben mentioned Vulcan’s work to build faster climate change models.