Sabrina González-Pasterski’s research on gravitational memory was cited by Stephen Hawking, a basic stage for a young scientist already known for building her own airplane at 14. Now 33, she works at the Perimeter Institute in Canada, where she focuses on celestial holography and how artificial intelligence might change the way physicists approach their field. She also leads the Celestial Holography Initiative, a project advancing this new direction in theoretical physics.
González-Pasterski wants to use AI to map the structure of physical knowledge, finding links between theories and surfacing questions that have not yet been asked. The idea draws on her experience moving between engineering and physics. She aims to use artificial intelligence to explore the space of physical theories and their relationships, while leaving solutions to physicists. She completed her doctorate at Harvard in 2019.
Her early interest in pushing boundaries took her to MIT, where she was among the first women to graduate in physics with the highest possible score. González-Pasterski draws a line between building something that works and understanding why it works. “As a physicist, you want to know why it works, not just that it does,” she says. That approach now drives her efforts to bridge quantum theory and Einstein’s relativity, two foundations of modern physics that still don’t fit together.
Her research on gravitational memory and the spin memory effect has opened new ways to think about how gravitational waves leave lasting marks on space-time. Seen after events such as black hole mergers, these effects offer clues about the universe’s underlying symmetries. Both effects are central to her research. Her findings suggest these traces could help physicists identify universal mathematical patterns, a step toward the long-sought quantum theory of gravity.
González-Pasterski recognizes the limits: spin memory alone won’t solve the puzzle. It is part of a larger effort to determine whether gravity can be described holographically, meaning information about our three-dimensional universe could be encoded on a two-dimensional surface such as the celestial sphere. This idea, called celestial holography, sits basic to her current work. She warns against taking the “universe as a hologram” metaphor too literally. For her, it is a mathematical tool, not a literal picture of reality.
Recent independent publications have noted that the intersection of artificial intelligence and fundamental physics is gaining traction as a research topic. Most applications remain experimental: researchers use AI to explore possible connections between physical theories, not to provide definitive answers.
Major hurdles remain. Theoretical physicists have yet to build a complete version of holography for flat space-time, the kind that best matches our universe. The real test, González-Pasterski says, is whether these ideas can describe what we see. She is working with experts in artificial intelligence and mathematics to create tools for exploring which physical theories are possible and how they connect.
Her family’s Cuban roots and early engineering projects have helped shape a path marked by a willingness to try new things, an advantage in a field where tools like AI are changing the field. González-Pasterski cautions that a research focus on automated answers could cost physics its core purpose: understanding. She argues that even if quantum gravity never leads to new technology, the search for knowledge is still worth it.
Labels like “the new Einstein” or “the Einstein Latina” once made her uneasy. Now she is willing to use the attention to bring more scientists into developing new tools. “Let them call me what they want,” she says, focusing instead on the need for the scientific community to engage with these changes.
The intersection of science and technology isn’t new. As reported earlier, shifts in scientific thinking often reflect broader changes in society and culture. González-Pasterski’s story shows that the future of physics may depend as much on new ways of thinking as on new discoveries. Her work points to a shift in how researchers tackle basic questions, with artificial intelligence likely to play a bigger role in the next era of research.
