Cosma ShaliziView profile
Associate Professor
Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.
