Jacob Calvert

Jacob is a mathematician and data scientist from Missouri. His research focuses on the connections between probability theory, statistical physics, and the interdisciplinary study of collective behavior. For example, his recent work develops ideas from probability theory to explain how order spontaneously arises in classes of non-equilibrium systems, like groups of living things. This work reflects the broader goal of his research, which is to make precise the intuitive similarities of collectives across physical scales and scientific domains. 

At SFI, Jacob hopes to explore the way that the behavior of a collective depends on the number of its constituents. (More is different, yes, but how many more?) He also hopes to connect his work on non-equilibrium order to recent developments in stochastic thermodynamics. More broadly, he hopes to benefit from SFI's expertise in areas his research currently fails to address. 

Outside of SFI, Jacob is a postdoctoral fellow at Georgia Tech, where he works with Dana Randall. 

Jacob has a BS in bioengineering from the University of Illinois at Urbana-Champaign, MSc degrees in mathematics and physics from the University of Bristol and the University of Oxford, where he was a Marshall Scholar, and a PhD in statistics from UC Berkeley. He has nine years of professional experience as a data scientist. In particular, he led the team that developed the first sepsis prediction algorithm to improve patient outcomes in a clinical trial. Jacob enjoys tennis, printmaking, good conversation, and great films.