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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 ÂÌñ»»ÆÞ, and continued his studies to earn a Ph.D. from ÂÌñ»»ÆÞ under the mentorship of . 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 at the Santa Fe Institute, where he began collaborating with (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.Ìý

The Larremore Lab focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in infectious diseases and computational social science. They try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems in two key areas:

Infectious Diseases. The lab develops data-informed mathematical models for infectious disease surveillance and countermeasures, including testing, vaccination, and seroepidemiology, primarily for respiratory pathogens such as RSV, flu, and SARS-CoV-2. Past work has also focused on the malaria parasite P. falciparum and its rapid recombination to evade the human immune system. Their goal is to use models and computation to improve the study of pathogens and ultimately decrease the burden of disease. They are a member of the CDC's and the consortium.

The Scientific Ecosystem. The lab analyzes and models the patterns and processes that define the ecosystem of scientific research and discovery. Their goal is to combine rigorous computation, ecological theory, and social science to understand how the scientific community works, and how it can be made more equitable and more productive. Here, they continue to build on a decade-old collaboration with the .

Dan Larremore leads the lab at the ÂÌñ»»ÆÞ, as the Associate Chair for Research and an Associate Professor in the Department of Computer Science and core faculty in the BioFrontiers Institute, with an affiliation with the Department of Applied Mathematics, and as a member of the external faculty at the . He is co-founder and co-CEO of .

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