Jiguo Cao, CRM-SSC Prize in Statistics 2021

Jiguo Cao
CRM-SSC Prize in Statistics

The CRM-SSC Prize in Statistics is awarded annually by the Centre de recherches mathématiques (CRM) and the Statistical Society of Canada (SSC) in recognition of outstanding research carried out primarily in Canada by a statistician during the first fifteen years after completing a PhD. The 2021 recipient of this prize is Jiguo Cao, Professor and Canada Research Chair in the Department of Statistics, and Actuarial Science at Simon Fraser University.


Jiguo was born in 1981 in a remote village in Shandong Province in China to parents who, though poor and illiterate instilled in him both a yearning for knowledge and an attitude to life: `grab any opportunity, put in maximum effort, and excel yourself’ – even if you cannot plan your life in advance.  Jiguo had never heard much about statistics before he entered the undergraduate program in statistics at Beijing Normal University in 1998.  He was consistently the top student despite a heavy load of private tutoring – a financial necessity.  The result was offers from several top American Ph.D. programs in 2002. But in the wake of 9/11 a US visa never appeared. Instead, Jiguo grabbed an opportunity to spend a year in the bioinformatics Ph.D. program at Peking University.  There he met and fell in love with his now wife and colleague, Liangliang Wang.  A year later he took up another opportunity and joined the statistics Ph.D. program at McGill University in 2003 under the supervision of Prof. James O. Ramsay, the 1998 SSC Gold Medalist.  Only after arriving in Montréal did Jiguo learn that Montréal is a francophone city but Jiguo again grabbed the opportunity and made the maximum effort.  He got married, earned perfect marks in 13 graduate courses, learned French and completed the PhD in 3 years without doing a master’s degree.


Jiguo then spent a year as a post-doctoral fellow in biostatistics at the School of Public Health at Yale University working with Professor Hongyu Zhao, the 2008 recipient of the Mortimer Spiegelman Award, and then joined the still new Department of Statistics and Actuarial Science at Simon Fraser University.  In 2012 he left SFU to join the Department of Statistical and Actuarial Sciences at Western University as an Associate Professor and Tier II Canada Research Chair. He was lured back to SFU in 2013 where he has been since. At SFU he was again awarded a Tier II Canada Research Chair (2015-2025). He was promoted to the rank of Professor in 2020.  


Jiguo’s thesis with Jim Ramsay at McGill was a key component in a truly major paper – a JRSS-B read paper with Ramsay, Giles Hooker and David Campbell. This work on a “generalized smoothing approach” to parameter estimation for differential equations attracted no fewer than 26 discussion contributions.  The procedure bypasses directly solving the differential equations, instead the strategy approximates the solution by a smoothing basis function expansion. The result was a whole new approach to fitting these models which has led to a great deal of further work by many groups.


A second outstanding contribution lies in “parameter cascading” (in which advantage is taken of the fact that different parameters have different roles in models — structural parameters of interest, nuisance parameters, and ‘complexity’ parameters), originating in a paper published with Jim Ramsay in the Journal of the American Statistical Association (JASA) in 2010.  Papers in Biometrics in 2011, in Journal of Computational and Graphical Statistics in 2012, and his 2013 Journal of Agricultural, Biological and Environmental Statistics (JABES) paper develop the power of the idea; they and other work have cemented the idea’s place in practice.  It seems wise to quote a bit from the glowing letters which supported the nomination for this award:  “a computational method superior to existing differential equation methods, resulting in faster computation and less sensitivity to the choice of initial conditions” and “an optimization algorithm that is amazingly simple to program and leads to fast and scalable computation” to cite just two. 


These papers are the tip of the iceberg.  There is a lovely 2013 JASA paper with Xiaolei Xun, Bani Mallick, Arnab Maity, and Ray Carroll which goes beyond ordinary differential equations models to partial differential equations and is widely praised.  We won’t continue in this vein; there is too much highly visible research to describe.  Instead, here is a numerical snapshot. In just 15 years, Jiguo has some 59 papers in statistical methodology journals, another 13 in applications journals and 4 more book chapters and conference proceedings.  Twice, his papers have been recognized as annual highlights by journal editorial boards (JABES in 2013 and Statistica Sinica in 2019).  It must be said, however, that a snapshot is all this is; with 27 additional methodology papers at various stages in the review process these numbers will be wrong almost immediately.


It seems appropriate to present a short list of ideas and areas of Jiguo’s work which excited his nominators for the CRM/SSC prize:  beyond the differential equations work discussed above there are many contributions to Functional Data Analysis (FDA) including the development of fSCAD for sparse functional regression, and a substantial body of work on functional principal component analysis with an important focus on making the output of this method interpretable.  Lately, Jiguo has begun work on combining modern statistical methods with FDA, principally by connecting more closely to methods for high dimensional data such as the LASSO and Deep Learning/Neural Networks.  On the applications side, he has important contributions to genetics, to fish management, to forest fire management and to a variety of areas in public health and medicine.  


This all adds up to an astounding record which has attracted a great deal of attention.  According to Google Scholar, Jiguo has been cited 2478 times (h-index = 23);  this is a striking number in our discipline, one where citation rates are notoriously low.


We at SFU are lucky to have Jiguo here.  Jiguo collaborates.  His departmental colleagues find him a wonderful person to work with.  But his collaboration goes far beyond departmental colleagues and students. He has many co-authors across SFU, across Canada, and around the world.  He produces key ideas in these research projects and then works hard on all the aspects of the publication process.  And he mentors brilliantly:  3 postdocs, 4 PhD and 12 MSc students, of whom 8 have secured academic jobs in Canada, China, Turkey and the USA.  One, Peijun Sang now at Waterloo won the Pierre Robillard Award for the best Canadian Ph.D. thesis in Statistics in 2019.


We should not end without pointing to Jiguo’s extensive service to the statistical community.  He is Associate Editor for 4 excellent journals: Biometrics (since 2018), The Canadian Journal of Statistics (since 2017), Journal of Agricultural, Biological, and Environmental Statistics (since 2017), and Statistics and Probability Letters (since 2017). He was a guest co-Editor of a CJS special edition on Functional Data Analysis.  Add in program committee service, workshop organization, session organization, CANSSI service on the Board and the SSC Finance Committee, and an outstanding record of presentations and overall he has a truly striking record. 


Jiguo credits his success to his inspirational mentors, collaborators, colleagues, students and supportive family. He is particularly grateful to his wife, Liangliang Wang, an associate professor at SFU, for extensive collaborations and personal support. They have three lovely kids: Angela, Andrew and Annika.  Jiguo enjoys outdoor activities with his family and friends such as hiking, skiing, and bicycling. He also likes investing; he managed to sell all his stocks right before the 2020 stock market crash! 

The citation for the award reads: 

“To Jiguo Cao for outstanding developments in modeling and analysis of functional data and dynamic systems; for broad  work in numerous applications with special focus on statistical genetics; and for remarkable aptitude for creating and nurturing  productive collaborations, particularly involving students and  post-doctoral fellows.”


Richard Lockhart and Liangliang Wang were primarily responsible for producing this material.