IDSS faculty and industry experts unpack how data can be used to lead, mislead, manipulate, and inform the public’s viewpoints and decisions. Season 2 has begun!

Learn data science methods and tools, get hands-on training in data analysis and machine learning, and find opportunities in a growing field. Watch our latest informational webinar.

This 10-week online program covers statistics and Python foundations, machine learning, prediction, recommendation systems, and more.

Bayesian nonparametrics provides modeling solutions by replacing the finite-dimensional prior distributions of classical Bayesian analysis with infinite-dimensional stochastic processes.

Causal inference: Geometry of conditional independence structures for 3-node directed Gaussian graphical models.

Learning problems that involve combinatorial objects are ubiquitous – they include the prediction of graphs, assignments, rankings, trees, groups of discrete labels or preferred sets of a user; the expression of prior structural knowledge for regularization, the identification of sets of important variables, or…

In this line of research, we develop strategies to optimize utility in dynamic environments in an optimal and efficient fashion.

Computational limitations of statistical problems have largely been ignored or simply overcome by ad hoc relaxations techniques.