Daniel Larremore

Daniel Larremore applies methods from network science to solve data-rich problems in diverse areas, ranging from genetic epidemiology to social systems. Mathematics, computational modeling, genomics, sociology, and evolutionary biology intermingle in Dr. Larremore’s dynamic and progressive research program. Larremore received a B.S. in chemical engineering from Washington University in St. Louis, an M.S. degree in applied mathematics from the University of Colorado Boulder, and continued his studies to earn a Ph.D. from CU Boulder under the mentorship of Dr. Juan Restrepo. In his graduate studies, Larremore analyzed networks of coupled excitable systems, a project directly related to neuroscience experiments in cortical tissue cultures. Dr. Larremore completed his postdoctoral training at the Harvard School of Public Health with Dr. Caroline Buckee, and as a prestigious Omidyar Fellow at the Santa Fe Institute, where he began collaborating with Aaron Clauset (CS). As a postdoc, Dr. Larremore expanded his use of statistical network models to trace the evolutionary history of the malaria parasite Plasmodium falciparum and developed mathematical models for analysis of prestige and bias in faculty hiring networks. 

Dr. Larremore returned to CU Boulder as an Assistant Professor in the Department of Computer Science and member of the BioFrontiers Institute core faculty in 2017. His research revolves around complex networks, most prominently Malaria’s antigenic evolution and academic labor market dynamics. The Larremore lab continues to research the malaria parasite P. falciparum with the goal of understanding how this parasite has evolved to evade detection by the human immune system. At the same time, Dr. Larremore applies large-scale data collection and generative models to examine influences on faculty hiring outcomes. This computational social science research program is interdisciplinary by nature, but Dr. Larremore also uses his expertise to track collaborative research and scientific productivity across disciplines. Using his interdisciplinary background to both create and apply sophisticated mathematical models and methods, Dr. Larremore illuminates the value of reaching between fields to solve fundamental biological and social problems.