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Dr. Stewart develops statistical methods for researchers who seek quick and efficient analyses of new and computationally challenging data. His research interests include developing methods for newly emerging data types that are motivated by scientific discovery (e.g. SNP intensity data of related individuals). Using both family and population based data, he has developed improved methods for mapping genetic risk variants for psychiatric disorder (e.g. genes and CNVs related to Alzheimer's disease and bipolar disorder, respectively), and for estimating genetic maps by combining information efficiently from multiple studies (i.e. meta-analyses). In addition, he has a general interest in deterministic math models, gene networks, and problems that arise in computational molecular biology. Most of his methods employ a rich variety of deterministic and stochastic algorithms, Monte Carlo sampling techniques, and various optimization procedures to estimate genetic quantities of interest (e.g. lod scores, kinships, CNV frequencies and carrier status) in fairly general settings.