Vidhi is a PhD student at the University of Cambridge, Department of Physics. Prior to this she completed a MPhil in Scientific Computing from Cambridge (2016) and a M.Sc. in Applicable Mathematics from the London School of Economics and Political Science (2010) securing Distinction in both. Before joining Cambridge in 2015 she worked as a quantitative analyst at Credit Suisse and as a high frequency trader at the Chicago based hedge fund, Citadel Securities (Europe) between 2011 and 2015. Her supervisors are Prof. Carl Edward Rasmussen and Dr. Christopher Lester from Cambridge.
Her research interests centre around approximate inference in Bayesian non-parametric models like Gaussian processes and Dirichlet processes. Hierarchical extensions of these models offer greater modelling flexibility, provide robust uncertainty estimation and allow one to capture complex interactions between variables typical in real world data. However, this additional flexibility comes at a price, effective inference in hierarchical models is computationally more intensive and exacerbated by the presence of strong correlations in the free variables at different levels of the hierarchy.
Uncertainty quantification in machine learning predictions is becoming increasingly mainstream as several applications in science and industry require statistical guarantees in the predictions generated by models. Bayesian non-parametrics is currently the only paradigm that allows the user to stipulate a prediction in terms of a probability distribution and allow automatic calibration of model complexity.
She is equally interested in the potential of incorporating machine learning in the science of discovery workflows in high energy physics and astronomy.