Causal inference, at the intersection of statistics and machine learning, is an active field of research that develops methods and algorithms for the data-driven derivation and analysis 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 ...

Data really powers everything that we do. Research activities in the data science area are concerned with the development of machine learning and computational statistical methods, their theoretical ...

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 ...

Faculty in the Statistical Learning and Data Science Hub advance statistical and machine learning methods tailored to the unique challenges of biomedical and epidemiologic data, including ...

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