Episodit
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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. She has been recognized with numerous awards, both for her research and her work to increase diversity, equity and inclusion in computing.
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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.
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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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.
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