mccormick.northwestern.edu

MLDS 490: Bayesian Methods for Inference and Decision Making

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...
Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...
Nature

A Bayesian perspective on observers’ inference of group norms

Inferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent ...
Nature

Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction

Groundwater is the planet’s largest distributed store of freshwater and a critical buffer against drought. Yet aquifers are still managed largely with static, deterministic vulnerability indices that ...
Helsinki

Research Overview

We develop novel methods to make Bayesian inference more efficient, scalable, and practical. This includes work on variational methods, Monte Carlo algorithms, and techniques for handling complex ...