Joshua's research interests involve improving practices in data science and machine learning to reduce the impact of bias, particularly biases associated with social harms and scientific reproducibility. This includes developing methods and software for statistical inference after model selection, and using causality to analyse the fairness and interpretability of algorithms in machine learning and artificial intelligence. More broadly, he is interested in high-dimensional statistics and causal inference, and in teaching theory, applications, and best practices in data science using the R statistical programming language. 

Before joining LSE Joshua earned his PhD in Statistics at Stanford University, was a Research Fellow at the Alan Turing Institute affiliated with the University of Cambridge, and then was an Assistant Professor at New York University from 2017-2020.