Research Interests
My research program aims to identify fundamental principles of organization for complex social and biological systems, and how data and computation can be used to illuminate them. Much of this work is organized around two themes. The first theme focuses on how interactions at smaller scales within a system drive regularities and dynamics at larger scales. For instance, how does the human malaria parasite maintain the functionality of its var genes despite pervasive and seemingly random genetic recombination, and what role do social networks play in driving or maintaining structural inequalities, such as gender imbalances, within the scientific workforce? Or, more generally, how does simplicity arise from complexity? The second theme focuses on developing advanced computational methods that can automatically characterize large-scale regularities and either connect them with causal mechanisms at smaller scales or exploit them to make predictions. These themes are broad and often cross disciplinary boundaries. My research program is thus highly interdisciplinary, drawing on and contributing to advances in network science, machine learn- ing, statistics, statistical physics, molecular biology, genetics, epidemiology, ecology, macroevolution, political science, sociology, and the science of science. A supplemental aim of my research is to advance computation as a third pillar of science, co-equal with mathematical theory and laboratory experiments as a method for generating new scientific knowledge.
Keywords: Network science (methods, theories, applications); Data science, statistical inference, machine learning; Models and simulations; Collective dynamics and complex systems; Rare events, power laws and forecasting; Computational social science; Computational biology and biological computation.
Teaching
I teach courses in Algorithms and in Network Science, and have previously taught an undergraduate seminar on The History and Future of Computing. I am a strong advocate of both the liberal arts and the broad utility of a deep quantitative training.
About Aaron Clauset
Aaron Clauset is an Assistant Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute.
Clauset is an internationally recognized expert on network science, data science, and machine learning for complex systems. His work has appeared in Nature, Science, PNAS, Ecology Letters, American Naturalist, SIAM Review, and Physical Review Letters. His work has also been covered in the popular press by the Wall Street Journal, The Economist, Discover Magazine, New Scientist, Wired, Miller-McCune, the Boston Globe and The Guardian.