Science in Parallel Podcast Por Krell Institute arte de portada

Science in Parallel

Science in Parallel

De: Krell Institute
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Science in Parallel focuses on people in computational science and their work simulating climate and the cosmos, understanding viral infections, building alternative energy strategies and more – using high-performance computing (HPC). Host Sarah Webb interviews researchers about their career paths and motivations. Our conversations cover topics such as artificial intelligence, integrating emerging hardware, the effects of remote work, promoting diversity and inclusion, and the role of creativity in computing. Our show is for curious, science-oriented listeners who like technology. You don’t need a deep background in science and computing to learn from our guests. Science in Parallel has been shortlisted for the Publisher Podcast Awards: for 2022 Best Technology Podcast, 2023 Best Science and Medical Podcast and both categories in 2024. It is produced by the Krell Institute and is a media outreach project of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program.Copyright 2023, Krell Institute, All Rights Reserved. Ciencia
Episodios
  • S6E4: Joel Ye: Examining Neural Data More Efficiently and Holistically
    Jun 18 2025

    Understanding how the brain works remains a grand scientific challenge, and it's yet another area where researchers are examining whether foundation models could help them find patterns in complex data. Joel Ye of Carnegie Mellon University talks about his work on foundation models, their potential and limitations and how others can get involved in applying these AI tools.

    Joel Ye is a Ph.D. student in the program in neural computation at Carnegie Mellon University in Pittsburgh, where he studies ways to understand brain data and brain-computer interfaces. He's a third-year Department of Energy Computational Science Graduate Fellow.

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    25 m
  • S6E3: Jackson Burns: Avoiding Chemical Dead Ends
    May 14 2025

    Chemists and chemical engineers have modeled molecules for decades, but artificial intelligence and foundation models offer the prospect that researchers could train models with predictive abilities in one area of chemistry that could be fine-tuned for another. Trustworthy chemistry foundation models could help streamline the experimental time and resources needed to discover new medicines or design new batteries. Massachusetts Institute of Technology Ph.D. student Jackson Burns is working on these questions. He describes the promise and challenges of building foundation models in chemistry, his work on chemprop, and his advice to other researchers interested in working on foundation models for chemistry and science in general.

    You'll meet:

    Jackson Burns is a Ph.D. student in William Green's chemical engineering group at MIT. He's also a third-year Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient. He completed his undergraduate degree in chemical engineering at the University of Delaware.

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    26 m
  • S6E2: Prasanna Balaprakash: Predicting Earth Systems and Harnessing Swarms for Computing
    Apr 16 2025

    In the second episode in our series on foundation models for science, we discuss Oak Ridge National Laboratory's work and hear about lessons learned from the recent 1000 Scientists AI Jam, a recent event that brought together researchers from several Department of Energy national laboratories, OpenAI and Anthropic. My guest is Prasanna Balaprakash, ORNL's director of AI programs. We talk about how foundation models could help with climate forecasts and his team's 2024 Gordon Bell finalist research and futuristic work that applies principles of swarm intelligence for managing distributed computing resources.

    Prasanna Balaprakash has been the director of artificial intelligence programs at Oak Ridge National Laboratory (ORNL) since March 2023. Previously he had worked as a postdoctoral researcher and staff computer scientist at Argonne National Laboratory. He was a 2018 recipient of a Department of Energy Early Career Research Program award.

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    31 m
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