Our upcoming candidate has a PhD in Mathematics.  The candidate's primary research interests include Applied Mathematics, Cardiovascular Research, Complex Systems, Computational Biology, Computational Social Science, Computational, Quantitative or Systems Biology, Machine Learning Statistics, Biostatistics and Infectious Disease.

Research talk "An Adaptive, Stacked, Multi-Model Ensemble to Forecast Seasonal Influenza Outbreaks”.  Influenza is an acute, highly contagious respiratory illness that infects 5-10% of adults and 20-30% of children globally every year. Multi-model ensemble forecasts, weighted combinations of individual component models, have been used by the US Centers for Disease Control and Prevention (CDC) for internal planning and public communication in the last few influenza seasons. We propose a new method for adaptively weighting a multi-model ensemble over the course of a season to improve forecasting accuracy. Our adaptive ensemble assigns a convex combination of weights to forecasting models, and includes a time-dependent, equally-weighted Dirichlet prior to shrink weights towards an equal weighting. A variational algorithm is used to fit our adaptive ensemble and aims to optimize the log score. Applied to forecasts of short-term ILI incidence at the regional and national level in the US, our adaptive model outperforms the equally-weighted ensemble, and has similar or better performance to the static ensemble, which requires multiple years of training data. Adaptive ensembles are able to quickly train and forecast during epidemics, and provide a practical tool to public health officials looking for forecasts that can conform to unique features of a specific season.