Science in Parallel

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.

• 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.

• 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.

• 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.

• 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.