The human brain is remarkably good at detecting patterns in the world around us. We notice behaviors, rhythms, and recurrences, and often build analogies to explain them. But not all of these intuitive ideas about nature hold up under mathematical scrutiny.
Visiting Complexity Postdoctoral Fellow Jacob Calvert is interested in which of those ideas can be made precise. A mathematician and data scientist, he studies how collective behaviors emerge and recur across different physical and biological systems. “I’m interested in simple, direct questions about nature that are difficult to answer precisely,” he says. “I try to see which analogies actually hold up to the microscope of precise mathematics.”
His research aims to test the structural similarities of collectives across physical scales and scientific domains. Drawing on a wide-ranging academic background, he uses tools from probability theory, statistical physics, and the study of collective behavior to bring mathematical clarity to ideas that often begin as a metaphor. In a parallel career as a data scientist at Dascena, he led the development of the first sepsis-prediction algorithm shown to improve patient outcomes in a clinical trial.
Calvert is exploring open questions around entropy production in non-equilibrium systems and how the behavior of collectives changes with scale. He’s also interested in connecting his probabilistic approach to non-equilibrium systems with recent developments in stochastic thermodynamics, and in engaging with areas where his current research could benefit from SFI’s broader expertise.
He holds a Ph.D. in statistics from UC Berkeley, M.Sc. degrees in mathematics (University of Bristol) and theoretical physics (University of Oxford), and a B.S. in bioengineering from the University of Illinois. He is also a postdoctoral fellow at Georgia Tech. Calvert traces his path to SFI back to reading Complexity: A Guided Tour by Melanie Mitchell as a college student. “It just changed my life,” he says. “The book was raising those questions about nature that were the most interesting, somehow the most obvious and fundamental, but the most difficult to answer.”