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Ethan Meitz and Jerry Wang of Carnegie Mellon University talk about molecular simulation, heat transfer and how conversation in many forms has shaped Ethan's Ph.D. research. Ethan is a mechanical engineering Ph.D. student and current DOE Computational Science Graduate Fellow, and Jerry is his Ph.D. advisor and a past fellowship recipient. This episode is the first in our Season 8 series, focusing on collaboration. You'll meet: Ethan Meitz a mechanical engineering Ph.D. student at Carnegie Mellon University and a fourth-year Department of Energy Computational Science Graduate Fellow (DOE CSGF). He works with Alan McGaughey, in the mechanical engineering department and Gerald (Jerry) Wang associate professor of civil and environmental engineering at Carnegie Mellon University. Jerry was a DOE CSGF recipient from 2014 to 2018.

On July 15, 2021, Science in Parallel launched a six-episode season celebrating the 30th anniversary of the Department of Energy Computational Science Graduate Fellowship program. After five years, 45 episodes and 55 guests, we're celebrating our coverage of computational collaboration and creativity, exploring research on energy, medicine, artificial intelligence, quantum science and more. You'll meet: Sarah Webb: Science in Parallel's host Anda Trifan: Senior Principal Scientist, GSK Amanda Randles: Professor of Biomedical Engineering, Duke University Anubhav Jain: Chemist/Staff Scientist/Engineer, Lawrence Berkeley National Laboratory Danilo Perez: Ph.D. student at New York University Ian Foster: Professor of Computer Science, University of Chicago, and Senior Scientist, Argonne National Laboratory Sunita Chandrasekaran: Associate Professor of Computer and Information Sciences, University of Delaware Quentarius Moore: Software Development Engineer, AMD

Recently four alumni of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) met and discussed quantum science and quantum computing. They also shared how the DOE CSGF helped their careers and their advice for new fellows and other early career computational scientists. To celebrate the 35th anniversary of the DOE CSGF, we've included their answers here as a bonus episode. We hope their insights will help other researchers deepen their careers. You'll meet: Jacob Bringewatt: Assistant Professor of Physics at the U.S. Naval Academy Grace Johnson: Senior Product Manager, NVIDIA Alicia Magann : Senior Member of Technical Staff, Sandia National Laboratories Dylan Sim: Senior Quantum Applications Architect, PsiQuantum

Quantum computing involves collaboration and interdisciplinarity, the meeting of minds from different perspectives to solve problems where their expertise overlaps. This episode does a version of that with audio, bringing together insider insights from four quantum researchers across industry, academia and the national labs. They discuss research areas including fundamental quantum mechanics, algorithms and calibration and the human and network connections that will be needed to build utility-scale quantum computers. All four guests are alumni of the Department of Energy Computational Science Graduate Fellowship program, which supports this podcast. You'll meet:  Jacob Bringewatt: Assistant Professor of Physics at the U.S. Naval Academy Grace Johnson: Senior Product Manager, NVIDIA Alicia Magann : Senior Member of Technical Staff, Sandia National Laboratories Dylan Sim: Senior Quantum Applications Architect, PsiQuantum

NVIDIA is known for its AI work, and in quantum computing the company focuses on integrating quantum processors with classical processors to accelerate quantum computing. In this conversation NVIDIA's Sam Stanwyck talks about the challenge and importance of quantum error correction, the company's work on integrating quantum and classical hardware and the partnerships with startup companies and the national laboratories that propel this research forward. You'll meet: Sam Stanwyck is the Director for Quantum Product at NVIDIA. He previously worked in quantum engineering at Rigetti Computing. He completed a Ph.D. in applied physics at Stanford University in 2017.

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.

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.

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.

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.

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.

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.

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).

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, paired with data from wearable devices, could lead to new ways to monitor and treat patients. She also talks about her public engagement and science policy work and its importance, both for educating patients and supporting computational science's future. You'll meet: Amanda Randles is the Alfred Winborne and Victoria Stover Mordecai associate professor of biomedical sciences at Duke University and director of Duke's Center for Computational and Digital Health Innovation. Her research using high-performance computing to model the fluid dynamics of blood flow has garnered numerous awards including one of the inaugural Sony Women in Technology Awards with Nature , the 2024 ISC Jack Dongarra Early Career Award and the 2023 ACM Prize in Computing. Amanda completed her Ph.D. at Harvard University working with Efthimios Kaxiras and Hanspeter Pfister. She was a Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient from 2010 to 2013 and a Lawrence Fellow at Lawrence Livermore National Laboratory from 2013 to 2015. Follow Amanda on social media: LinkedIn, BlueSky and Instagram.

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.