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).

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.

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.