I began my career as a biologist, but found that every question that interested me could only be answered by solving an even more intriguing statistical puzzle. Thus my career path veered into statistics. However, much of my work, both theoretical and applied, remains motivated by my scientific training.
The collaborative research I have enjoyed most is in the area of statistical genetics. Right now, a topic that interests me greatly is the use of statistical tools to understand the workings of the human genome and the nature of inherited diseases.
The connecting theme in my research is "mixture models," which I see as a way to model the heterogeneity in nature. Traditional statistical models attempt to explain the mean response of each individual in the study. Frequently, however, this is not possible because key variables may not be directly observable. For example, genetic differences among patients may lead them to respond differently to a drug therapy. In some instances one asks  is there heterogeneity in the population or not?
My results for mixture models have been used to better understand a broad range of scientific phenomena. This, for me, is the most satisfying aspect of statistics, when methods you develop are applied to answer important scientific questions.
I am looking for a post doc to work on projects applying cutting edge methods in statistics and machine learning to solve scientific problems emerging from genetics, using modern largescale genomics data such as singlecell sequencing data. We are looking for highly motivated individuals with a strong background in statistical methodology/theory, and a genuine interest in the science and datadriven research. The successful candidate must have (1) a Ph.D. in statistics, biostatistics, computational biology, computer science or other related quantitative field, (2) strong computing skills, (3) good communication skills. Interested candidates should send an email inquiry.
