Cosma Shalizi is an assistant professor of statistics at Carnegie Mellon University, where his research focuses on aspects of the statistical analysis of complex systems: nonlinear prediction algorithms, heavy-tailed distributions, contagion in networks, and self-organizing processes. Previously, he was a post-dcotoral fellow at the University of Michigan’s Center for the Study of Complex Systems and at the Santa Fe Institute, where he is now an external faculty member. He got is Ph.D. in theoretical physics from the University of Wisconsin-Madison in 2001.
By this expert
Time Series Forecasting: Model Evaluation and Selection Using Nonparametric Risk Bounds
We derive generalization error bounds — bounds on the expected inaccuracy of the predictions — for traditional time series forecasting models.
Featuring this expert
Why Economics Needs Data Mining
Cosma Shalizi urges economists to stop doing what they are doing: Fitting large complex models to a small set of highly correlated time series data.