I was drawn to the discipline of statistics by a fascination with randomness, and by the way it blends mathematics and scientific content. I have enjoyed interactions with researchers in many areas, especially the natural sciences, which have given me opportunities to learn about these fields and to make contributions to them. Underlying these separate analyses are paradigms of statistical methodology that have been developed over the last 100 years. This evolution accelerates as statisticians confront new challenges in the information age.

I am especially interested in formulating methods for analyzing data that arise in the form of random functions, such as time series, and which involve large quantities of data and computationally intensive analysis.

Much of my recent work has centered around two projects in astronomy: detecting objects in the outer regions of the solar system (the Kuiper Belt) and detecting gamma-ray pulsars. Small bodies with radii >100 km have recently been detected beyond Neptune using large telescopes. The purpose of the Taiwanese American Occultation Survey (TAOS) is to measure directly the number of these objects (KBO's) down to the typical size of cometary nuclei (a few km). When a KBO moves in between the earth and a distant star it will block the starlight momentarily. A telescope monitoring the starlight will thus see it blinking. The probability of such occultation events is so low that it will be necessary to conduct 100 billion measurements per year in order to detect the ten to four thousand such occultation events expected. Thus, foremost among the statistical problems is the necessity of developing methods to detect very rare, faint events in very large quantities of data.

A gamma-ray pulsar is a rotating neutron star that emits gamma-ray photons. A statistical challenge is to infer from a sequence of arrival times of such photons, whether the source is periodic, corresponding to a pulsar, or whether it is constant, corresponding to background radiation.