Science in Parallel

Krell Institute

Science in Parallel focuses on people in computational science and their interdisciplinary research to solve energy challenges, discover new materials, model medicines and more — using high-performance computing (HPC) and artificial intelligence. Host Sarah Webb interviews researchers about their career paths and motivations. Our conversations cover topics such as integrating emerging hardware, the effects of remote work, the role of creativity in computing and foundation models in science. 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 and 2025. 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.

  1. MAR 18

    S7E2: Megan Ivory: Supporting the Quantum Workforce

    Science in Parallel

    42 min

  2. MAR 4 ·  BONUS

    Jarrod McClean (Bonus): Parsing Logical Qubits

    Quantum computing comes with a new layer of concepts. Quantum bits are called qubits, but there's more. Physical qubits are often grouped to form logical qubits. In our recent conversation with Jarrod McClean, we discussed logical qubits. And we're sharing that discussion as a Science in Parallel short.

    8 min

  3. FEB 18

    S7E1: Jarrod McClean: Designing Quantum Algorithms

    In our seventh season, we're putting a spotlight on quantum computing, technology that could help speed up high-performance computing and artificial intelligence, shore up cybersecurity, study complex natural systems and much more. Jarrod McClean works on quantum algorithms and applications at the Google Quantum Artificial Intelligence laboratory, and this conversation links some of the ideas about AI for science from our last season to emerging quantum technology. Join us for a conversation about Jarrod's work at Google, where he thinks quantum computing could soon enter the computational science workflow and the mental gymnastics of harnessing hardware that researchers are still designing.

    32 min

  4. 12/10/2025

    S6E10: Sunita Chandrasekaran: Computation in Translation

    Computational science requires translation, breaking ideas and principles into pieces that algorithms can parse. The work requires experts capable of zooming in on core computer science while also being able to step back and make sure that the big scientific questions are addressed. This guest, Sunita Chandrasekaran of the University of Delaware, moves seamlessly across these layers— from working with students and postdocs on fundamental software, collaborating with researchers on questions ranging from physics to art conservation and helping to shape AI policy in her state. In our conversation, we discuss the rapid pace of artificial intelligence, the synergy among academia, the national labs and industry, and keeping humans at the center of AI innovation. You'll meet: Sunita Chandrasekaran directs the First State AI Institute at the University of Delaware and is an associate professor of computer and information sciences. She is also the vice-chair of Delaware's state AI commission. She has worked as a computational scientist at Brookhaven National Laboratory and served on the U.S. Department of Energy's Advanced Scientific Computing Advisory Committee. During a sabbatical, she completed two visiting researcher stints in industry, first at Hewlett Packard Enterprise and then at NVIDIA. Sunita was named the 2025 Emerging Woman Leader in Technical Computing by the Association of Computing Machinery's Special Interest Group on High Performance Computing.

    39 min

  5. 11/12/2025

    S6E9: Silvia Crivelli: Understanding Suicide Risk and Building a Foundation Model for Medicine

    Nearly a decade ago, the U.S Department of Veterans Affairs and the Department of Energy launched the MVP-CHAMPION initiative, not for sports, but as a data-driven strategy for improving healthcare outcomes for veterans and others. Silvia Crivelli of Lawrence Berkeley National Laboratory turned her skills in computational biology toward this new field, especially the problem of identifying veterans at high risk for suicide. As she and her colleagues worked on this challenge, large language models and the notion of foundation models emerged. Now her team is focused on a more comprehensive challenge: a foundation model for medicine and healthcare. You'll meet: Silvia Crivelli is a staff scientist in the applied computing for scientific discovery group at Lawrence Berkeley National Laboratory, where she's worked for more than 25 years. Her research applies artificial intelligence to medicine and healthcare with the goal of combining biomolecular and clinical data. She works on the MVP-CHAMPION research initiative between the U.S. Department of Veterans Affairs and the Department of Energy, focuses on precision medicine for veterans and the broader population.

    40 min

  6. 10/15/2025

    S6E8:Youngsoo Choi: Building Reliable Foundation Models

    Foundation models-- LLMs or LLM-like tools-- are a compelling idea for advancing scientific discovery and democratizing computational science. But there's a big gap between these lofty ideas and the trustworthiness of current models. Youngsoo Choi of Lawrence Livermore National Laboratory and his colleagues are thinking about to how to close this chasm. They're engaging with questions such as: What are the essential characteristics that define a foundation model? And how do we make sure that scientists can rely on their results? In this conversation we discuss a position paper that Youngsoo and his colleagues wrote to outline these questions and propose starting points for consensus-based answers and the challenges in building foundation models that are robust, reliable and generalizable. That paper also describes the Data-Driven Finite Element Method, or DD-FEM, a tool that they've developed for combining the power of AI and large datasets with physics-based simulation. You'll meet: Youngsoo Choi is a staff scientist at Lawrence Livermore National Laboratory (LLNL) and a member of the lab's Center for Applied Scientific Computing (CASC), which focuses on computational science research for national security problems. Youngsoo completed his Ph.D. in computational and mathematical engineering at Stanford University and carried out postdoctoral research at Stanford and Sandia National Laboratories before joining Livermore in 2017.

    31 min

  7. 09/10/2025

    S6E7: Steven Wilson: Craving Chemical Efficiency

    Computational scientists can take on the role of utility players in research, and Steven Wilson is one example. At Arizona State University he's built instruments, carried out experiments and dove deep into computational work. As a postdoc, he's working on a new challenge: building a quantum chemistry startup company. In this episode, he discusses his career that started with 10 years in the United States Navy Nuclear Program, how that military experience shaped his academic studies and the role of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) in shaping his research to make chemical reactions more efficient. You'll meet: Steven Wilson is a postdoctoral researcher in Christopher Muhich's research group at Arizona State University, where he completed both his undergraduate degree in 2020 and his Ph.D. in 2024. He was a DOE CSGF recipient from 2021 to 2024 and completed practicum research at Pacific Northwest National Laboratory (PNNL). He is also CEO of PsaiForge, a quantum chemistry software startup that he cofounded with Muhich.

    26 min

  8. 08/13/2025

    S6E6 [REPOST]: Joe Insley Transforms Big Data into Stunning Images

    While we take a short summer break, we're posting one of our favorite past episodes and a great follow-up to our last episode with Amanda Randles of Duke University. In 2023, we talked with Joe Insley of Argonne Leadership Computing Facility and Northern Illinois University about data visualization, from the practical process of helping researchers understand their results to showstopping images and animations that make the work accessible to broad audiences. Joe discusses his career path, how he and his team approach visualization projects, his work with students and his advice for improving scientific data visualization. You'll meet: Joe Insley is team lead for visualization and data analysis at Argonne Leadership Computing Facility and associate professor in the School of Art and Design at Northern Illinois University. Joe got his start in scientific visualization creating interactive data explorations for the CAVE (cave automatic virtual environment).

    30 min
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Trailer

5
out of 5
3 Ratings

About

Science in Parallel focuses on people in computational science and their interdisciplinary research to solve energy challenges, discover new materials, model medicines and more — using high-performance computing (HPC) and artificial intelligence. Host Sarah Webb interviews researchers about their career paths and motivations. Our conversations cover topics such as integrating emerging hardware, the effects of remote work, the role of creativity in computing and foundation models in science. 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 and 2025. 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.

Information

  • Creator
    Krell Institute
  • Years Active
    2021 - 2026
  • Episodes
    42
  • Rating
    Clean
  • Copyright
    © Copyright 2023, Krell Institute, All Rights Reserved.
  • Show Website
    Science in Parallel

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