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 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. 

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 chemistry, his work on chemprop, and his advice to other researchers interested in working on foundation models for chemistry and science in general. You'll meet:  Jackson Burns is a Ph.D. student in William Green's chemical engineering group at MIT. He's also a third-year Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient. He completed his undergraduate degree in chemical engineering at the University of Delaware.

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 computing resources. Prasanna Balaprakash has been the director of artificial intelligence programs at Oak Ridge National Laboratory (ORNL) since March 2023. Previously he had worked as a postdoctoral researcher and staff computer scientist at Argonne National Laboratory. He was a 2018 recipient of a Department of Energy Early Career Research Program award.

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 for the future of science? This episode begins a series where we'll explore these questions and more, talking with computational scientists about their work with foundation models and the opportunities and challenges in this exciting, rapidly changing area of research. We'll start by talking with Ian Foster of Argonne National Laboratory and the University of Chicago about AuroraGPT, a foundation model being developed for science and named for Argonne's new exascale computer. You'll meet: Ian Foster is a senior scientist at Argonne National Laboratory where he directs the data science and learning division. He's also a professor of computer science at the University of Chicago. He is the co-leader of the data team for Argonne's AuroraGPT project.  

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 our recent interview with Anil Ananthaswamy. You'll meet:  Mansi Sakarvadia is a Ph.D. student in the computer science department at the University of Chicago and a current Department of Energy Computational Science Graduate Fellow. She studies ways to interpret how machine learning models work. Josh Vermaas is an assistant professor at Michigan State University. His research in computational structural biology focuses on understanding photosynthesis and energy transfer processes in plants as part of the MSU-DOE Plant Research Laboratory.

2024 was artificial intelligence's Nobel Prize year with the physics and chemistry prizes recognizing the underpinnings and application of these algorithms. Science journalist and author Anil Ananthaswamy spent years writing a popular book, Why Machines Learn: The Elegant Math Behind Modern AI, that explores the equations and historical context for this technology. In this conversation, Anil and host Sarah Webb explore that math and history, the significance of these Nobel Prizes for both AI and science, and the challenges that come with this powerful and fast-moving technology. You'll meet: Anil Ananthaswamy is an award-winning journalist and journalist-in-residence at the Simons Institute for the Theory of Computing at the University of California, Berkeley. Previously he has worked as a staff writer and editor for New Scientist magazine. He has written four books including Why Machines Learn: The Elegant Math Behind Modern AI (Dutton, 2024).

The annual Supercomputing meeting (SC24) convenes November 17-22 in Atlanta with the theme of HPC creates, and Science in Parallel previews a special display at the meeting: the Art of HPC. Host Sarah Webb interviews Sadie Bartholomew of the United Kingdom's National Centre for Atmospheric Science and the University of Reading about her work as a research software engineer and her passion for creative coding. She submitted several pieces of digital art that will be displayed at SC24. Sadie discussed the many patterns in her work—within weather and climate, in coding and in digital art. She makes her pieces using matplotlib, a visualization tool in Python. She talks about the synergy and fulfillment she finds at the interface of computing and aesthetic pursuits.

Early in her applied math journey, Paulina Rodriguez was a little skeptical of calculators and computers. But her desire to really understand what's going on under the hood has ultimately led to satisfying research. During her Ph.D., she's explored the credibility of computational models for medical device applications, making sure that researchers understand the accuracy, validity and uncertainty of simulated results. Paulina shares how she honed her problem-solving skills and creativity as she navigated her education. Her enthusiasm and determination are infectious, and she describes her personal struggle to bring her whole self to her work. You'll meet: Paulina Rodriguez, a Ph.D. student in applied math at George Washington University and a fourth-year recipient of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF). Paulina completed her bachelor's degree at University of California, Santa Cruz and master's degree at Claremont Graduate University, both in mathematics. Her current research focuses on establishing methods for assessing the credibility of computational models for medical device applications, work that she's doing at Sandia National Laboratories in New Mexico in collaboration with the U.S. Food and Drug Administration.   Episode artwork created using ChatGPT from prompts by Paulina Rodriguez.

Science communication often attracts people with diverse interests, who thrive in multiple roles. Paul Sutter is no exception: he's an astrophysicist, host, author and more. He's also a visiting professor at Barnard College, Columbia University. Paul's roots are in computational science, and he shares how his many projects continue to build on that foundation. We also discuss his most recent book: Rescuing Science: Restoring Trust in an Age of Doubt, which critiques today's scientific enterprise and and offers ideas for supporting a better future. You'll meet: Paul M. Sutter is a theoretical cosmologist, science communicator, media host, NASA advisor and U.S. cultural ambassador. He is currently a visiting professor at Barnard College, Columbia University. He completed his physics Ph.D. in 2011 at the University of Illinois Urbana-Champaign, where he was supported by a Department of Energy Computational Science Graduate Fellowship. He also held a joint position as chief scientist at the Center of Science and Industry in Columbus, Ohio, and as a cosmological researcher at the Ohio State University.

Video games are everywhere, but the fundamental elements that generate human reactions such as suspense or surprise aren't understood. Instead, game designers start from scratch each time they want to build a new experience for players. Rogelio Cardona-Rivera of the University of Utah wants to understand games and the fundamental elements that make people respond as they do—as a science of games. The research is important for more than just gaming—Rogelio is working on a variety of projects, including artificial intelligence research, technology for Indigenous storytelling and virtual reality in math education. Join us for a conversation about the emerging field of technical games research that also dives into the creative and communications challenges of working at the bleeding edge of disparate fields: computer science, cognitive science, narrative and more. You'll meet: Rogelio Cardona-Rivera is an assistant professor of games at the University of Utah. Rogelio completed their Ph.D. at North Carolina State University in 2019, supported by a Department of Energy Computational Science Graduate Fellowship and funding from the National GEM Consortium. Their undergraduate degree is in computer engineering from the University of Puerto Rico at Mayagüez. 

The field of high-performance computing (HPC) currently faces dual challenges: important technical problems that require a skilled workforce and the need to recruit more computational researchers. This conversation with Lois Curfman McInnes of Argonne National Laboratory examines both the complexity in building scientific software and the work needed to build the HPC workforce of the future. You'll meet: Lois Curfman McInnes is a senior computational scientist in the mathematics and computer science division at Argonne National Laboratory. She served as deputy director for the software technology focus are of the U.S. Department of Energy's Exascale Computing Project and completed her Ph.D. in applied mathematics at the University of Virginia.  

Artificial intelligence is reshaping research to discover new materials for a range of important applications. In this episode, meet Anubhav Jain of Lawrence Berkeley National Laboratory, a researcher who has been at the forefront of this transition. He uses machine learning and other computational tools as a materials scientist to discover compounds that could store and convert energy and solve other societal problems. Anubhav's current research path started in graduate school at MIT, where he was supported by a Department of Energy Computational Science Graduate Fellowship. We discuss how computational tools including AI have moved from a novel idea to a central piece of materials discovery, how he applies machine learning tools to other tasks such as mining data from scientific papers, and the rewards that came from writing his blog called Hacking Materials. This episode concludes our season 4 series on creativity in computing.  

Sometimes extraordinary circumstances like the pandemic offer researchers unexpected opportunities to serve others. Danilo Pérez, now a Ph.D. student in computational neuroscience at New York University, found himself in this situation in Puerto Rico in 2020. He contributed his mathematical modeling expertise as part of a team that built and maintained Puerto Rico's public health data during that intense period. Later he contributed to AI-based modeling of coronavirus variants that won major honors in the computing community: the 2022 Gordon Bell Special Prize for HPC-Based COVID-19 Research. These days Danilo is developing computational tools to understand value-based decision making at NYU, a process that can be applied in economics, medicine and public policy. We discuss how compelling science problems have propelled his training, how music and family support him, and his focus on citizen-facing science, especially in Puerto Rico. You'll meet: Danilo Pérez, a Ph.D. student in computational neuroscientist jointly advised by Christine Constantinople and Cristina Savin in NYU's Center for Neural Science. He is a current recipient of a Department of Energy Computational Science Graduate Fellowship (DOE CSGF). This conversation was recorded in July 2023 at the Annual Program Review of the DOE CSGF in Washington, D.C. Read more about Danilo and his work in DEIXIS.

Traditional science career advice often urges people to specialize and become the best at one activity. But that perspective can undervalue interdisciplinary researchers and other polymaths who can see connections between and beyond science and engineering fields. This episode's guest, Casey Berger, describes how she has navigated this second approach, embracing her many interests, such as science, computing, teaching and storytelling, to make her mark as a physicist and data scientist and as a fiction author. In the second episode of our podcast series on creativity in computing, Casey talks about her path to physics and computing via Hollywood. She describes the challenges and opportunities of interdisciplinary work, how she pursues her many interests and her advice for building a sustainable, joyful life and career. You'll meet: Casey Berger is an assistant professor of physics and data science at Smith College in Northampton, Massachusetts. She completed her Ph.D. at the University of North Carolina at Chapel Hill in 2020 and was supported by a Department of Energy Computational Science Graduate Fellowship (DOE CSGF).  She earned bachelor's degrees in physics from Ohio State University and in philosophy and film production from Boston University. Casey is also a science fiction author. Her latest novel Sister from the Multiverse, part of the Choose Your Own Adventure series, was published in October 2023. This conversation was recorded in July 2023 at the Annual Program Review of the DOE CSGF in Washington, D.C.

Season 4 of Science in Parallel centers around creativity and computing, starting with an interview about climate modeling. At this nexus of physics, earth science, mathematics and computing, researchers are also racing against the clock to accurately predict how global climate is shifting before the changes happen. Pulling all the scientific pieces together and communicating those results so that others can use them are significant creative challenges—ones that both Tapio Schneider and Emily de Jong of California Institute of Technology have embraced. In our conversation, Tapio and Emily describe how both the science and societal impact of climate modeling motivate them, how outdoor activities and music shape their perspectives, and how they view creativity both inside and outside the lab. Later in the episode, Tapio shares his experience as a science advisor to the ClimateMusic Project—an artists' collaboration that's producing music and video pieces that explore climate change and solutions to the climate crisis. You'll meet: Tapio Schneider is a professor of environmental science and engineering at Caltech. He's a member of the Climate Modeling Alliance (CLiMA) a team of scientists, engineers and applied mathematicians from Caltech, MIT and NASA's Jet Propulsion Laboratory working on a new earth system model that uses computatational and data-science tools to harness Earth observations and make more accurate climate predictions. He spoke about that research at the 2023 Annual Program Review of the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program in July. Emily de Jong is a Ph.D. student in mechanical engineering at Caltech working in Tapio's research group. She is a DOE CSGF recipient, who completed her undergraduate degree at Princeton University in 2019.

The exascale era in computing has arrived, and that brings up the question of what's next. We'll discuss some emerging processor technologies-- molecular storage and computing, quantum computing and neuromorphic chips—with an expert from each of those fields. Learn more about these technologies' strengths and challenges and how they might be incorporated into tomorrow's systems.  You'll meet: Luis Ceze, professor of computer science at the University of Washington and CEO of the AI startup OctoML. Bert de Jong, senior scientist and department head for computational sciences at Lawrence Berkeley National Laboratory and deputy director of the Quantum Systems Accelerator.  Catherine (Katie) Schuman, is a neuromorphic computing researcher and an assistant professor of computer science at the University of Tennessee, Knoxville.

Although he's always loved space, Gabriel Casabona pursued other fields, including medicine and religion, before landing in astrophysics. We discussed how his passion for physics motivated him to deepen his knowledge of math and computing, how gravity's mysteries define his work and other big challenges he hopes to work on during his career. You'll meet: Gabriel Casabona is a Ph.D. student in computational and theoretical astrophysics at Northwestern University. His work is supported by a Department of Energy Computational Science graduate fellowship. This conversation was recorded in person in November 2022 at the SC22 meeting in Dallas, Texas.

In early December 2022, Lawrence Livermore National Laboratory announced that the National Ignition Facility (NIF) had achieved fusion ignition—a reaction of merging hydrogen isotopes that produced more energy than the lasers put in. High-performance computing is an important part of designing, analyzing and refining these experiments, and this episode examines the connection between computing and fusion energy. You'll meet: Tammy Ma, a plasma physicist at Livermore, talks about how supercomputing supported fusion ignition. Tammy also leads the lab's Inertial Fusion Energy Initiative. Tammy's scientific expertise is doing experiments rather than simulations, but in her current role she considers all parts of the fusion puzzle. She's at the forefront of one of science and society's grand challenges: Can we produce clean, sustainable fusion energy on the scale needed to power our planet? Tammy talks about computing's role in understanding and optimizing fusion reactions and how computing's crossroads could shape fusion's future.

Even after enjoying her first computer science course, Margaret Lawson wasn't convinced she'd have a place in the field. But today she works on cloud storage for Google after completing her Ph.D. at the University of Illinois, Urbana-Champaign, where she was supported by a Department of Energy Computational Science Graduate Fellowship (DOE CSGF). This conversation was recorded at the Supercomputing meeting (SC22) in Dallas in November 2022, where Margaret co-led a  Birds of a Feather (BoF) session on Ethics in High Performance Computing. We talked about that session, her pursuit of challenging computer science problems and progress for women in computing. You'll meet: Margaret Lawson is a software engineer based in Google's Kirkland, Washington, office. There she primarily works on cloud storage platforms.  

Making sense of computational science takes a multidisciplinary team, including science visualization experts who translate data into images that both parse information so that it's comprehensible and render it into beautiful images and skillful animations. Joe Insley of Argonne Leadership Computing Facility and Northern Illinois University has been doing this work for more than 20 years, leveraging deep training in both digital art and computer science to build showstopping visualizations. We talked about his training, how he approaches this work and how in situ visualization—techniques that allow computational researchers to sift through data as it's processed—is changing with ever larger supercomputers.  

Science in Parallel's season two concludes with a conversation about answering important questions in biology and medicine with leadership class supercomputers, including urgent issues that came up during the COVID-19 pandemic. You'll hear from Anda Trifan of the University of Illinois, Urbana-Champaign and Amanda Randles of Duke University. Starting as a chemist, Anda is completing a Ph.D. in biophysics and quantitative biology at the University of Illinois Urbana-Champaign where she has studied molecular strategies that make certain cells turn cancerous. In early 2020, she joined an Argonne National Laboratory team that pivoted to working on the pandemic, and she modeled how SARS-CoV-2 infects cells, how it replicates and how it spreads through aerosols. Amanda is an assistant professor of biomedical engineering at Duke University with roots in physics and computer science. Much of her work now focuses on large-scale simulations of how blood flows through a person's unique network of vessels. During the pandemic, her team applied their expertise to calculations that could help physicians figure out how to split ventilators between patients who weren't exact matches, a critical problem in early 2020 when these devices were in short supply. Both Anda and Amanda completed Department of Energy Computational Science Graduate Fellowships. Between them, they have worked on a total of five projects that have been finalists for either the ACM Gordon Bell Prize or the Special Prize for COVID-19 research. Adding to the excitement of their pandemic work: They both navigated the at-home adventure of raising very young children during lockdown. They talk about what drives them, the challenge of working at the cutting edge of HPC and biology and medicine, and their advice for other researchers, particularly other women in science.

Valerie Taylor doesn't shy away from challenging problems with multiple layers. At Argonne National Laboratory, she manages teams that develop algorithms, data management strategies, software and hardware to support scientific simulations, including those on the Department of Energy's leadership-class supercomputers. Her research focuses on performance analysis—the factors involved in making computations efficient. On top of that, she maintains a parallel line of work supporting computer scientists from historically marginalized communities toward building a more diverse computing workforce. You'll hear Valerie talk about her career path, what excites her about computing, and the sustained commitment needed to boost diversity, equity and inclusion in this field. You'll meet: Valerie Taylor is the director of the mathematics and computer science division at Argonne National Laboratory. She moved to Argonne in 2017 after more than 25 years in academia at both Northwestern University and at Texas A&M University. She also is the president and chief executive officer of the Center for Minorities and People with Disabilities in IT (CMD-IT), a non-profit dedicated to supporting historically marginalized communities in computing. 

After COVID-19 lockdowns and 2020 wildfires near his Oregon home, computational scientist Jeff Hammond decided to make big moves. In 2021, his family of five emigrated from Portland to Finland, and Jeff changed positions, leaving Intel and taking a new job with NVIDIA. Even before 2020, he had worked primarily remotely and discusses the lessons he hopes technology companies learn from pandemic work. You'll meet: Jeff Hammond, a principal engineer with NVIDIA, is affiliated with the company's office in Helsinki, Finland. From 2014 to 2021, Jeff worked for Intel, and was based in Portland, Oregon. Prior to that he worked at Argonne National Laboratory. Jeff was a Department of Energy Computational Science Graduate Fellowship recipient from 2005 to 2009 at the University of Chicago and focused on developing open-source chemistry simulation software, NWChem, with Karol Kowalski at Pacific Northwest National Laboratory.

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.

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.

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.

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. 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.

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).

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.  

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.  

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.

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.

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.