7th Annual MIT Policy Hackathon
Apply by October 11 to join this year’s in-person hackathon taking place on November 15-17. Applicants will be notified about their status by October 18, 2024.
Podcast: Data Nation
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!
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. Watch our latest informational webinar.
Data Science and Machine Learning: Making Data-Driven Decisions
This 10-week online program covers statistics and Python foundations, machine learning, prediction, recommendation systems, and more.
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.
