Season 2, Episode 1 -- Future of Work (part 1): Communication Conundrum
Release Date: 06/01/2022
S7E5: Celebrating Five Years of Science in Parallel
Science in Parallel
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: Science in Parallel’s host : Senior Principal Scientist, GSK : Professor of Biomedical Engineering, Duke University : Chemist/Staff Scientist/Engineer, Lawrence Berkeley National...
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Quantum Quartet (Bonus): DOE CSGF Insights and Career Advice
Science in Parallel
Recently four alumni of the (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: : Assistant Professor of Physics at the : Senior Product Manager, : Senior Member of Technical Staff, : Senior Quantum Applications Architect, ...
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S7E4: Quantum Quartet: Insider Insights Toward Fault-Tolerant Systems
Science in Parallel
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 program, which supports this podcast....
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S7E3: Sam Stanwyck: Quantum Error Correction and Research Partnerships
Science in Parallel
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: is the Director for Quantum Product at NVIDIA. He previously worked in quantum engineering at Rigetti Computing. He completed a...
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S7E2: Megan Ivory: Supporting the Quantum Workforce
Science in Parallel
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Jarrod McClean (Bonus): Parsing Logical Qubits
Science in Parallel
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.
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S7E1: Jarrod McClean: Designing Quantum Algorithms
Science in Parallel
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...
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S6E10: Sunita Chandrasekaran: Computation in Translation
Science in Parallel
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...
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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...
info_outlineIn our first two episodes of Science in Parallel’s Season 2, we’ll be talking about how the pandemic pivot to remote work marks a turning point in workplace structure for many computational scientists. We talk with computational scientists who worked remotely about what they struggled with, what functioned well and the lessons they’ll take into the future.
In this first part, we’ll also focus on the social science of how people experienced remote work.
In part one, you’ll meet:
Jerry Wang is an assistant professor of civil and environmental engineering at Carnegie Mellon University. He was a Department of Energy Computational Science Graduate Fellowship recipient from 2014 to 2018 while pursuing his Ph.D. at Massachusetts Institute of Technology. Jerry works on particle-based simulations to study soft and active matter, for applications ranging from nanoscale devices to pedestrian mobility.
Elaine Raybourn is a social scientist in Sandia National Laboratories’ Applied Information Sciences Center. She is also an institutional principal investigator for one of the DOE Exascale Computing Project’s many individual research teams: Sandia’s interoperable design of extreme-scale application software (IDEAS) team. IDEAS focuses on team of teams, software developer productivity and software sustainability.
From the episode:
Elaine has organized the ECP’s Strategies for Working Remotely panel series since 2020. Check out their slides and videos about topics such as setting up a home office space, parenting, working with interns and hybrid work.
The increased use of video conferencing during pandemic lockdowns highlighted the problem of degraded communication, a concept that is commonly called “Zoom fatigue.”
You can also read more from Elaine about how ECP members experienced remote work and how they coped with the loss of office whiteboards.
A version of the interview with Elaine Raybourn is also available as an ASCR Discovery article.