I joined Davis in January of 2004. Before that, I worked in the Statistics and Data Mining group at Bell Labs, the research arm of Lucent Technologies. I graduated from U.C. Berkeley in 1997 with a Ph.D. in Statistics, primarily in statistical computing systems.
While trained in statistics, the focus of my research is innovations in information technology and integrating computer science research concepts with the process of scientific and statistical research. The goal is to think about where statistical computing should be in the future, e.g. 5 to 10 years from now, and to determine how to get practitioners there. While we develop software for the present practitioners, my primary motivation is enabling things in the future and not merely reimplementation of existing concepts. The hope is to enable statisticians to do things that they currently cannot attempt, to export statistical methodology to non-statisticians, and to facilitate interchange and direct collaboration between disciplines.
Research topics include
An important aspect of my work is to facilitate the integration of software from different communities. It is important that statisticians be able to easily access both software and data from other disciplines. Similarly, it is vital that people in other fields using their familiar tools be able to use state-of-the-art statistical methodology developed by statisticians in their favorite environment, and be able to integrate them into their work process. I have developed a general and flexible model for integrating programming languages and systems to enable this interchange. We call this the inter-system interface model. This involves embedding systems within others, and also enabling the possiblity of programming using multiple languages.
I returned to academia from industrial research with the purpose of introducing modern statistical computing to the statistics curriculum. Rather than focussing almost exclusively on mathematical statistics, it is becoming increasingly important that students and researchers be able to work with data both in new formats and also in the increasing size we are continually experiencing. Data technologies, software development and basic computing proficiency are as important as statistical methodology in the context of the scientific process.