Science in Parallel

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This podcast has
10 episodes
Date created
Average duration
28 min.
Release period
38 days


Computers and science are intertwined – and not just as tools that help humans connect and collaborate. With computers, scientists model the earth’s climate, design alternative energy strategies and simulate exploding stars. From laptops to the world’s fastest supercomputers, software innovations and artificial intelligence are reshaping how we interact with mounds of data from healthcare to high-energy physics and how we solve critical problems. Computational science brings together mathematics, computer science and hardware and science expertise to take on these challenges. In this podcast, you’ll meet the scientists doing this work, learn more about their research and gain insights into the workings of this dynamic field.

Podcast episodes

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Two PhDs + Pandemic + Baby
Pandemic work was especially challenging for computational scientist parents, who often juggled new work arrangements while balancing their children's care. In this episode you’ll hear from a couple who were Ph.D. students and had a 10-month-old baby when lockdowns sent them all home in March 2020. The situation challenged their work and their mental health. As they adapted to these experiences, they changed career paths and their perspectives on life and work. You’ll meet: Kalin Kiesling is a nuclear engineer in the nuclear science and engineering division at Argonne National Laboratory. Her work focuses on the development of computational tools used to design the next generation of nuclear reactors. Prior to joining Argonne, Kalin earned her Ph.D., M.S., and B.S. in nuclear engineering and engineering physics from the University of Wisconsin-Madison. Brian Cornille is a member of technical staff at Advanced Micro Devices. He works on porting and performance optimization of scientific applications targeting AMD platforms, such as Frontier at Oak Ridge National Laboratory and the upcoming El Capitan at Lawrence Livermore National Laboratory. Brian was a DOE CSGF recipient from 2016 to 2020 and completed both a B.S. and Ph.D. in nuclear engineering and engineering physics at the University of Wisconsin-Madison.
Future of Work (part 2): Adapting to Change
In Season 2 of Science in Parallel, we’re examining how pandemic shutdowns have reshaped computational science workplaces. In our last episode we focused on the effects of virtual work and how the Exascale Computing Project’s Strategies for Working Remotely panel series fostered communication and creativity. This episode brings in additional stories from graduate students, a professor and an early career researcher at a DOE national lab about the challenges and benefits of remote work. You’ll meet: Episode one guests Elaine Raybourn of Sandia National Laboratories and Jerry Wang of Carnegie Mellon University. Jason Torchinsky is a Ph.D. student in applied mathematics at the University of Wisconsin-Madison and a third-year DOE CSGF recipient. They work on methods for applying parallel computing in climate models, particularly integrating disparate models to simulate the Madden-Julian Oscillation, an area of high and low moisture that moves around the Earth’s atmosphere every 30 to 60 days. Hilary Egan joined the National Renewable Energy Laboratory’s Computational Science Center as a data scientist in June 2020. Hilary completed her Ph.D. in astrophysics and planetary science at the University of Colorado Boulder and was a DOE CSGF recipient from 2014 to 2018. Hilary works on AI for scientific computing across applications including materials science, data center efficiency, and building retrofits. Laura Nichols is a second-year DOE CSGF recipient and a Ph.D. student in computational solid-state physics at Vanderbilt University. She uses quantum mechanics to model how defects in semiconductor devices are activated and lead to degradation. Laura is incorporating that model into her group’s code that describes defect-related processes such as scattering and electron capture.
Future of Work (part one): Communication Conundrum
In 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.
Season One, Episode Six -- Aurora Pribram-Jones
Aurora Pribram-Jones works on hot, dense electrons – simulating extreme chemistry that can happen within giant planets like Jupiter or nuclear fusion experiments. Aurora’s career included many initial detours on the way to science, but the flexibility of community college classes and a job at a technical bookstore paved their path toward research. Now a member of the chemistry faculty at the University of California, Merced, Aurora finds purpose in teaching and mentoring students and supporting the whole scientist, especially those from underrepresented and marginalized communities. Aurora completed a Ph.D. at the University of California, Irvine, and was a DOE CSGF recipient from 2011 to 2015. They carried out postdoctoral research at the University of California, Berkeley, and at Lawrence Livermore National Laboratory, the latter supported by a Lawrence Postdoctoral Fellowship. Aurora received the Frederick A. Howes Scholar Award in Computational Science in 2016.
Season One, Episode Five -- Alternative Energy
Avoiding the changing climate’s most extreme impacts will require a technological revolution to power daily life from renewable sources. An entrepreneur, an engineering professor and a DOE-laboratory materials scientist – all DOE CSGF and Massachusetts Institute of Technology alumni – discuss technical challenges from nuclear energy to heat transfer to hydrogen generation and the importance of choosing high-impact research problems. In addition to talking about science, engineering and computation, they highlight the need for a strong social and political movement to drive a complete overhaul of our energy infrastructure. You’ll meet: Leslie Dewan is a nuclear engineering entrepreneur and venture capitalist, who is currently the CEO of RadiantNano, a startup focused on radiation detection, identification and imaging. Asegun Henry is an MIT associate professor of mechanical engineering. What he calls his “sun in a box” design could lead to a viable system for storing renewable energy for the electrical grid. Brandon Wood is the associate program lead for Hydrogen and Computational Energy Materials at Lawrence Livermore National Laboratory and deputy director of the Laboratory for Energy Applications for the Future (LEAF).
Season One, Episode Four -- Alicia Magann
Alicia Magann got her start in control systems engineering research, exploring tools for controlling large-scale chemical processes. As a Ph.D. student, she turned the dials of quantum chemistry in Herschel Rabitz’s research group at Princeton University with support from the DOE CSGF. She talks about her work on quantum algorithms, her cross-country road trip from New Jersey to her practicum in California and how her dad is her scientific hero. Read more about Alicia and her work in the 2021 issue of DEIXIS.  
Season One, Episode Three -- Quentarius Moore
Curiosity, mentors and a summer working in concrete with his grandfather shaped Quentarius Moore’s science career studying 2-D materials. He recently completed his fourth year as a DOE CSGF recipient, while pursuing a chemistry Ph.D. at Texas A&M University. He completed both his bachelor's and master's degrees in chemistry at Jackson State University in Mississippi. Read more about Quentarius and his graduate research in the 2021 issue of DEIXIS magazine.  
Science in Parallel- Season One Trailer
Welcome to Science in Parallel, a new podcast about people and projects in computational science. Science in Parallel is produced by the Krell Institute, and season one celebrates the 30th anniversary of the Department of Energy Computational Science Graduate Fellowship Program.
Season One, Episode Two -- Artificial Intelligence and Climate Change
One of today’s hottest areas of computational research could help build better solutions for one of global society’s steepest challenges. Three early career computational scientists talk about AI’s potential for understanding and predicting climate shifts, supporting strategies for incorporating renewable energy, and engineering other approaches that reduce carbon emissions. They also describe how AI can be misused or can perpetuate existing biases. Working at this important research interface requires broad knowledge in areas such as climate science, public policy and engineering coupled with computational science and mathematics expertise. These early career researchers talk about their approaches to bridging this gap and offer their advice on how to become a scientific integrator. You’ll meet: Priya Donti is a Ph.D. student at Carnegie Mellon University, pursuing a dual degree in public policy and computer science, and a 4th year DOE CSGF recipient. She is also a co-founder and chair of the volunteer organization, Climate Change AI, which provides resources and a community for researchers interested in applying artificial intelligence to climate challenges. Priya was named to MIT Technology Review’s 2021 list of Innovators Under 35.  Read more about Priya and her work in the 2021 issue of DEIXIS. Kelly Kochanski completed a Ph.D. in geological sciences at the University of Colorado, Boulder in 2020 and works as a senior data scientist in climate analytics at McKinsey & Company. Kelly was a DOE CSGF recipient from 2016 to 2020, and her graduate research was featured in the 2020 issue of DEIXIS. She also is profiled in the 2021 issue as one of this year’s recipients of the Frederick A. Howes Scholar Award. Ben Toms also finished his Ph.D. last year at Colorado State University studying atmospheric science and is a 4th year DOE CSGF recipient. He has founded a company, Intersphere, that provides weather and climate forecasts up to a decade into the future. From the episode: Kelly and Priya contributed to the review article: Tackling Climate Change with Machine Learning, which was published on the arXiv preprint server in 2019. In the discussion about interpretable AI, Priya mentioned an article by Cynthia Rudin: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Ben mentioned Vulcan’s work to build faster climate change models.
Season One, Episode One -- Jeff Hittinger: Leading By Example
Jeff Hittinger of Lawrence Livermore National Laboratory embodies the term scientist-chimera. He talks about the many scientific hats he’s worn simultaneously – computer scientist, applied mathematician and physicist. As director for the Center for Applied Computing (CASC) and as co-principal investigator for the DOE CSGF, he wears many more. He talks about scientific success, leadership and the tricks he’s cultivated for communicating science to broader audiences through the Livermore Ambassador Lecture series. Jeff was a DOE CSGF recipient from 1996 to 2000 while earning his Ph.D. in aerospace engineering and scientific computing at the University of Michigan. He was one of the first recipients of the Frederick A. Howes Scholar Award and received the 2021 James Corones Award in Leadership, Community Building and Communication.

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5 out of 5
2 reviews
repthrd 2021/07/20
Great Interview Podcast About Science!
Short episodes that are to the point and have great content and interesting topics in the fields of science!


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