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 ...

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

The use of machine learning in statistics production is being explored widely, with applications including coding, outlier detection, and imputing missing values. Relatively little work has so far ...

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are ...

Chongzhi Di develops statistical learning methods for analyzing complex, high-dimensional, and real-time data in biomedical and behavioral research. His work focuses on wearable devices, accelerometry ...

The Francis College of Engineering, Department of Mechanical Engineering, invites you to attend a Doctoral Dissertation defense by Elyas Irankhah on: "Machine Learning and Causal Inference for ...