Art Owen, SSC Gold Medalist 2021

Art Owen
SSC Gold Medalist
2021

This year’s recipient of the Gold Medal of the Statistical Society of Canada is Art Owen. This prestigious award is bestowed upon a person who has made outstanding contributions to statistics, or to probability, either to mathematical developments or in applied work. It is intended to honor current leaders in their field.

 

Art Owen was born in Montreal in 1958.  A few months later, his family moved to the Ottawa area where he grew up.  Growing up there made it natural to spend a lot of time skiing and canoeing.  Outreach from the University of Waterloo to Canadian high school students is probably what tipped his interests from biology or forestry to mathematics, which is largely indoor. The dedication of his statistics, math and computer science educators at Waterloo gave him a life long unfair advantage as an academic in statistics, that holds up decades later. 

 

Art earned a Bachelor of Mathematics from University for Waterloo in 1981, and a PhD in Statistics from   Stanford in 1985. He was then hired the next year as an Assistant Professor at Stanford.  This was evidence of his great potential, as that department does not generally hire its own graduates, usually waiting 5-10 years before even considering them.

 

During his PhD studies, Art developed an interest in non-parametrics, and soon after (1988) invented the field of Empirical Likelihood. In his nomination letter, Christian Robert wrote that "This notion has opened a new area of non-parametric estimators, quite distinct from kernel methods ‘a la Parzen and Rosenblatt with a much higher degree of robustness and hence a wider applicability in poorly or partly specified models.'' This field has blossomed since then, culminating in his 2002 monograph on the topic.

 

Art's other major area of interest has been Monte Carlo and Quasi Monte Carlo methods. As with all of his research, his work in this area is grounded in mathematics but with a strong focus on applications. In his letter, Alexander Keller talked about Art’s seminal work on quasi-Monte Carlo using randomly scrambled nets and sequences, and says that “Evolutions of Art Owen’s work are at the core of many graphics algorithms to simulate light transport”.  In his letter, Jeff Rosenthal raved about Art’s contributions to MC and QMC, saying “It is fair to say that Art’s work on quasi-Monte Carlo has fundamentally changed how such algorithms are viewed and understood and used by leading Monte Carlo researchers everywhere.”

 

Ian Sloan from University of South Wales in Australia describes himself not as a Statistician but as “a mathematician with interests in computational mathematics, constructive approximation and physics.” In his letter he discussed Art inventions of what is now called “Owen scrambling” for QMC, as a “a major contribution to the field, one of permanent significance.” From this, it is clear than Art has had impact well beyond Statistics, in Applied Mathematics and beyond.

 

At Stanford, Art has graduated 23 PhD students, many of whom have gone on to significant careers in Academia and industry. One example is Ya Xu (2010), overseeing a thriving group of 250 Data Scientist at LinkedIn. He also worked tirelessly for the Department of Statistics and is currently serving as its Chair. Professionally he has made many contributions to the Monte Carlo community, including organizing conferences and symposia.


Art's work has previously been recognized in the First David Sprott Distinguished Lecture (2014), and the ASA Gottfried Noether Senior Scholar Award (2020) and he is a Fellow of the ASA and IMS.

 

He has deep gratitude for the support of his wife, Patrizia, and sons Greg and Elliot.

 

The citation for the award reads: 

“For his fundamental contributions to Statistical Science, especially his invention of Empirical Likelihood and his advances in Quasi-Monte Carlo methods, his training of graduate students and contributions to the Statistical Profession.”

 

Rob Tibshirani was primarily responsible for producing this material.