Science in Parallel

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.

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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.

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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).

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.

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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.

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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.

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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.

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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.

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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.