Senior research fellow and lecturer, University of Sydney

Roman Marchant's research at the Centre for Translational Data Science explores developing new data science techniques to answer complex questions in the social sciences, currently focusing on predicting crime and understanding criminal behaviour. His area of expertise is Sequential Bayesian Optimisation (SBO), which is a novel probabilistic method for optimal sequential decision-making under uncertainty that maximises long-term reward. Although SBO has been readily applied to robotics and environmental monitoring, it can be applied to any optimisation problem. Roman is exploring the application of SBO to increase the efficiency and reduce bias in predictive policing.
Dr Marchant completed a PhD at the School of Information Technologies, University of Sydney in 2015.

Experience

  • –present
    Lecturer in Machine Learning, University of Sydney

Education

  • 2016 
    University of Sydney, PhD in Machine Learning