# Research

### MicroMasters in Statistics and Data Science

Learn data science methods and tools, get hands-on training in data analysis and machine learning, and find opportunities in a growing field. Courses launch fall 2018.

### Nonparametric Bayesian Statistics

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

### Causal inference and applications to learning gene regulatory networks

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

### Combinatorial learning with set functions

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 inference in discrete probabilistic models.

### Online Learning

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

### Statistical and Computational Tradeoffs

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