Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has ...

For our current edition of “Video Highlights” I’d like to offer this talk that will review a series of recent papers that develop new methods based on machine learning methods to approach problems of ...

The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Pursuing computer science (CS) was a no-brainer for Zach Wood-Doughty. A third-generation computer science professor following in his father and grandfather’s footsteps, he was hooked at a very early ...

With the emergence of huge amounts of heterogeneous multi-modal data, including images, videos, texts/languages, audios, and multi-sensor data, deep learning-based methods have shown promising ...

The surge in enterprise AI has fueled interest in causal analysis. In this piece, I explore the threads that bind cause and effect - and how they can be applied across a range of industry scenarios.

The manufacturing landscape is evolving rapidly, with intelligent systems increasingly promising to boost efficiency, quality, and overall competitiveness. Traditional machine learning (ML) has ...

In the global conversation about modern modeling, digital transformation, and artificial intelligence, many names have become synonymous with groundbreaking advancement. Cynthia Rudin’s research on ...

Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed our ability to decode complex ...