S6E9: Silvia Crivelli: Understanding Suicide Risk and Building a Foundation Model for Medicine
Release Date: 11/12/2025
S6E9: Silvia Crivelli: Understanding Suicide Risk and Building a Foundation Model for Medicine
Science in Parallel
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...
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S6E8:Youngsoo Choi: Building Reliable Foundation Models
Science in Parallel
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...
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S6E7: Steven Wilson: Craving Chemical Efficiency
Science in Parallel
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 (DOE CSGF) in shaping his research to make chemical reactions more efficient. ...
info_outline![S6E6 [REPOST]: Joe Insley Transforms Big Data into Stunning Images show art](https://assets.libsyn.com/item/37777700?height=50&width=50)
S6E6 [REPOST]: Joe Insley Transforms Big Data into Stunning Images
Science in Parallel
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...
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S6E5: Amanda Randles: A Check-Engine Light for the Heart
Science in Parallel
Duke University associate professor Amanda Randles' work to simulate and understand human blood flow and its implications demonstrates how high-performance computing paired with scientific principles can help improve human health. In this conversation, she talks about how she brought together early interests in physics, coding, biomedicine and even political science and policy and followed her enthusiasm for the Human Genome Project. She discusses how supercomputers are pushing the boundaries of what researchers can learn about the circulatory system noninvasively and how that knowledge,...
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S6E4: Joel Ye: Examining Neural Data More Efficiently and Holistically
Science in Parallel
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. 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
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S6E3: Jackson Burns: Avoiding Chemical Dead Ends
Science in Parallel
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...
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S6E2: Prasanna Balaprakash: Predicting Earth Systems and Harnessing Swarms for Computing
Science in Parallel
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...
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S6E1 - Ian Foster: Exploring and Evaluating Foundation Models
Science in Parallel
Large language models aren't just powering chatbots like ChatGPT. This type of computational model is an example of a particular flavor of artificial intelligence known as foundation models, which are trained on vast amounts of data to make inferences in new areas. Although text is one rich data source, science offers many more from biology, chemistry, physics and more. Such models open up a tantalizing new set of research questions. How effective are foundation models for science? How could they be improved? Could they help researchers work on challenging questions? And what might they mean...
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S5E7 - Computational Scientists Discuss 2024 Nobel Prizes
Science in Parallel
Wrapping up our discussion of the 2024 Nobel Prizes in Physics and Chemistry, computer scientist Mansi Sakarvadia and computational structural biologist Josh Vermaas talk about the recent prizes and what they mean for science. You'll hear about how the prizes both break down research barriers and introduce concerns about misinformation and public trust. The research honored with the chemistry prize has already changed how researchers study questions that involve understanding proteins' structures. For more on the 2024 Nobel Prizes, check out . You'll meet: is a Ph.D. student in the and...
info_outlineNearly 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.