I have been an Assistant Professor of Biostatistics at Rochester since 2016. I am interested in methods and applications of single cell gene expression assays. There is great hope, for good reason, that measuring expression profiles in single cells will aid in immunological and cancer research, and provide new insight in cellular biology. However, the biochemical, computational and statistical challenges are sizable. I am interested in combinations of all three of these aspects.
I also collaborate closely with rheumatologists studying auto-immune disease and neonatologists studying the impacts of early life exposures on the immune system. One the most enjoyable things I have found about biostatistics is the opportunity to learn more about various disciplines in biology. Immunology has proved to be a fascinating one for me.
The past several years, I have taught a course on high-dimensional data analysis, which introduces the theory and application of many techniques used in machine learning, artificial intelligence and data science. I think that statisticians have a special perspective to contribute on this topic, since heart of statistics is how reason about a population from a sample. Often in data science, it’s tempting to start and end with the sample (which is often a large one). Statistics provides notions of sampling and selection biases, and ways to think about cause and effect from observational data that deserve wider dissemination.