Episodios
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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).
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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.
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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.
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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.
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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. Their grant funding includes a CAREER award from the National Science Foundation (NSF).
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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, especially those from underrepresented communities. 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.
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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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