Season 1, Episode 2 -- Artificial Intelligence and Climate Change
Release Date: 07/15/2021
Season 4, Episode 4 -- Anubhav Jain: Hacking Materials
Science in Parallel
Artificial intelligence is reshaping research to discover new materials for a range of important applications. In this episode, meet of Lawrence Berkeley National Laboratory, a researcher who has been at the forefront of this transition. He uses machine learning and other computational tools as a materials scientist to discover compounds that could store and convert energy and solve other societal problems. Anubhav’s current research path started in graduate school at MIT, where he was supported by a . We discuss how computational tools including AI have moved from a novel idea to a central...
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Season 4, Episode 3 -- Danilo Pérez: Embracing Versatility
Science in Parallel
Sometimes extraordinary circumstances like the pandemic offer researchers unexpected opportunities to serve others. Danilo Pérez, now a Ph.D. student in computational neuroscience at New York University, found himself in this situation in Puerto Rico in 2020. He contributed his mathematical modeling expertise as part of a team that built and maintained Puerto Rico’s public health data during that intense period. Later he contributed to AI-based modeling of coronavirus variants that won major honors in the computing community: the 2022 Gordon Bell Special Prize for HPC-Based COVID-19...
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Season 4, Episode 2 -- Casey Berger: Choose Your Own Multidimensional Career
Science in Parallel
Traditional science career advice often urges people to specialize and become the best at one activity. But that perspective can undervalue interdisciplinary researchers and other polymaths who can see connections between and beyond science and engineering fields. This episode’s guest, Casey Berger, describes how she has navigated this second approach, embracing her many interests, such as science, computing, teaching and storytelling, to make her mark as a physicist and data scientist and as a fiction author. In the second episode of our podcast series on creativity in computing, Casey...
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Season 4, Episode 1 -- Creativity in Climate Modeling
Science in Parallel
Season 4 of Science in Parallel centers around creativity and computing, starting with an interview about climate modeling. At this nexus of physics, earth science, mathematics and computing, researchers are also racing against the clock to accurately predict how global climate is shifting before the changes happen. Pulling all the scientific pieces together and communicating those results so that others can use them are significant creative challenges—ones that both Tapio Schneider and Emily de Jong of California Institute of Technology have embraced. In our conversation, Tapio and Emily...
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Season 3, Episode 5 -- Beyond Exascale: Exploring Emerging Hardware
Science in Parallel
The exascale era in computing has arrived, and that brings up the question of what’s next. We’ll discuss some emerging processor technologies-- molecular storage and computing, quantum computing and neuromorphic chips—with an expert from each of those fields. Learn more about these technologies’ strengths and challenges and how they might be incorporated into tomorrow’s systems. You’ll meet: , professor of and CEO of the AI startup . , senior scientist and department head for computational sciences at and deputy director of the . , is a neuromorphic computing...
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Season 3, Episode 4 -- Gabriel Casabona: It All Comes Down to Gravity
Science in Parallel
Although he’s always loved space, Gabriel Casabona pursued other fields, including medicine and religion, before landing in astrophysics. We discussed how his passion for physics motivated him to deepen his knowledge of math and computing, how gravity’s mysteries define his work and other big challenges he hopes to work on during his career. You’ll meet: is a Ph.D. student in computational and theoretical astrophysics at Northwestern University. His work is supported by a Department of Energy Computational Science graduate fellowship. This conversation was recorded in person in November...
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Season 3, Episode 3 -- Tammy Ma: Fusion Ignition and Beyond
Science in Parallel
In early December 2022, Lawrence Livermore National Laboratory announced that the (NIF) had achieved fusion ignition—a reaction of merging hydrogen isotopes that produced more energy than the lasers put in. High-performance computing is an important part of designing, analyzing and refining these experiments, and this episode examines the connection between computing and fusion energy. You’ll meet: , a plasma physicist at Livermore, talks about how supercomputing supported fusion ignition. Tammy also leads the lab’s . Tammy’s scientific expertise is doing experiments rather than...
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Season 3, Episode 2 –- Margaret Lawson: Finding Her Place
Science in Parallel
Even after enjoying her first computer science course, Margaret Lawson wasn’t convinced she’d have a place in the field. But today she works on cloud storage for Google after completing her Ph.D. at the University of Illinois, Urbana-Champaign, where she was supported by a Department of Energy Computational Science Graduate Fellowship (DOE CSGF). This conversation was recorded at the Supercomputing meeting (SC22) in Dallas in November 2022, where Margaret co-led a on Ethics in High Performance Computing. We talked about that session, her pursuit of challenging computer science...
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Season 3, Episode 1 -- Joe Insley: Big Data to Beautiful Images
Science in Parallel
Making sense of computational science takes a multidisciplinary team, including science visualization experts who translate data into images that both parse information so that it’s comprehensible and render it into beautiful images and skillful animations. Joe Insley of Argonne Leadership Computing Facility and Northern Illinois University has been doing this work for more than 20 years, leveraging deep training in both digital art and computer science to build showstopping visualizations. We talked about his training, how he approaches this work and how in situ visualization—techniques...
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Season 2, Episode 6 -- Pushing Limits in Computing and Biology
Science in Parallel
Science in Parallel’s season two concludes with a conversation about answering important questions in biology and medicine with leadership class supercomputers, including urgent issues that came up during the COVID-19 pandemic. You’ll hear from Anda Trifan of the University of Illinois, Urbana-Champaign and Amanda Randles of Duke University. Starting as a chemist, Anda is completing a Ph.D. in biophysics and quantitative biology at the University of Illinois Urbana-Champaign where she has studied molecular strategies that make certain cells turn cancerous. In early 2020, she joined an...
info_outlineOne 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 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.
You’ll meet:
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 2020 issue of DEIXIS. 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.