About
I come from New Zealand and completed Bachelor of Science and Bachelor of Commerce degrees at the University of Auckland. I received my PhD from the Department of Statistics at Stanford University. My advisor at Stanford was Trevor Hastie. I was a Professor in the Data Sciences and Operations department at USC Marshall from 1998 to 2022. Over the last ten years I have started to move into more administrative roles because I enjoy helping our amazing faculty and staff to have a bigger impact on their students and, through their research, the rest of the world. I still enjoy working on my own research. Below I list some of my research areas.
Functional Data Analysis
The key tenet of Functional Data Analysis (FDA) is to treat the measurements of a function or curve not as multiple data points but as a single observation of the function as a whole. This approach allows one to more fully exploit the structure of the data. FDA is an inherently multidisciplinary area and is becoming increasingly important as technological changes make it more common to observe functional data. A common FDA situation involves a functional regression problem where one might observe a response Y and a functional predictor X(t) which is measured over time or some other domain. The goal would then be to build a model to predict Y based on X(t). Fitting such a model is more challenging than for standard linear regression because the predictor is now an infinite dimensional object.
High Dimensional Statistics
Traditionally statistics has involved getting information from relatively small data sets involving perhaps on the order of a hundred observations and ten predictors or independent variables. However, recent technological advances in areas as diverse as web based advertising, finance, supermarket bar code readers (linked to customer cards) and even micro-arrays in genetics, have led to an entirely new type of data called High Dimensional Data. This data typically has anywhere from ten to a few hundred observations but possibly up to tens of thousands of variables. Dealing with such data poses very significant statistical and computational challenges. Trying to find the one or two important variables among thousands with only say 10 observations is roughly analogous to the traditional “finding a needle in a hay stack” with the added challenge that you only get 10 guesses before your time is up. HDD has become one of the most important areas of research in statistics.
Statistical Problems in Marketing
There are many interesting statistical problems in the marketing field. My major goal here is to incorporate new ideas from the statistical literature to provide solutions to practical marketing problems. For example, in one paper I used methods from the functional data analysis literature to accurately predict market penetration of 21 new products over 70 different countries. In another paper my coauthors and I suggested a new statistical methodology for predicting the trajectory of new technologies over time. We collected data over time for an extensive set of technologies and showed that our approach was overall more accurate than well known laws such as Moore's Law.