Born in 1978, Linglong Kong grew up in the village of Xuchang in Henan province, China. He studied probability and statistics at Beijing Normal University, where he obtained his bachelor's degree in statistics in 1999. He then completed his master's degree in statistics at Peking University in 2002. His MSc dissertation, supervised by Professor Zhonjie Xie, was titled “Monte Carlo Filter and an Application in a Signal Modulated Model.”
Linglong then joined the University of Alberta, where his PhD dissertation “On Multivariate Quantile Regression: Directional Approach and Application with Growth Charts” was supervised by Professor Ivan Mizera. Even before publication, the thesis earned Kong and Mizera an invitation to be discussants of a related paper in the Annals of Statistics. Linglong continued this work, as a postdoctoral fellow at Michigan State University, under the supervision of Yijun Zuo.
Of Linglong's work in robustness, a letter supporting the nomination reads: “His work, widely cited in the literature, stimulated much of the recent research on multivariate quantiles and multivariate depth. A recent publication integrated quantile regression and copula modelling to develop a new spatial quantile function-on-scalar regression model. This is one of the few significant pieces of work on the quantile analysis of functional response, and the method is useful in the analysis of image data.”
There followed a second postdoctoral fellowship at the University of North Carolina (Chapel Hill), under the supervision of Hongtu Zhu. It was here that Linglong's interests mushroomed, from robustness to neuroimaging data analysis, functional data, and statistical machine learning. Indeed, a hallmark of Linglong's career has been the breadth of his research, and his ability to synthesize results from diverse areas.
Since he joined the faculty at the University of Alberta in 2012, Linglong's record has been nothing less than breathtaking. At this writing, his CV shows over 80 published or in-press refereed journal papers in first rate outlets, and over 40 refereed papers presented at conferences with generally very low acceptance rates. There is as well quite a phenomenal list of invited presentations and short courses. His mentorship has been outstanding—at last count, he is supervising eight post-docs, 15 PhD students, six MSc students and has graduated a multitude of others who are now pursuing successful careers of their own in research or the commercial sector. A letter writer says, “His mentorship fosters independence, inclusivity, and a culture of excellence. He has allowed his students to engage in high-profile research projects, resulting in numerous coauthored publications in top-tier journals and conferences. This dedication has cultivated a new generation of statistical scientists who continue to advance the field. His trainees' successes reflect his exceptional ability to inspire and nurture talent; their achievements are a testament to his commitment to their professional growth.”
Linglong is the principal investigator or Co-PI on national and international grants totalling over \$3500K. It is an indicator of the impact of his research that \$2000K of these are CIHR grants jointly held with researchers in our medical school. He has made outstanding contributions to the profession in the form of editorial work. He is associate editor at each of the Journal of the American Statistical Association, Annals of Applied Statistics, International Journal of Imaging Systems and Technology, Statistics and Its Interface, and The Canadian Journal of Statistics (CJS), and was a guest editor of a special issue on neuroimaging data analysis in CJS as well as guest associate editor at Frontiers in Neuroscience.
Linglong was promoted to associate professor in 2018 and to full professor in 2022. He has this year become a fellow of the American Statistical Association. He is already an internationally recognized researcher in statistical machine learning and statistical optimization—he is AI Chair in the Canadian Institute for Advanced Research (CIFAR), for which he is based at the Alberta Machine Intelligence Institute (https://www.amii.ca) where he is a fellow. This follows his 2020 appointment as Canada Research Chair in Statistical Learning, based on his work in neuroimaging data analysis, with contributions to ensemble and hierarchical modelling, matrix factorization, and distributional reinforcement learning. More recently, he has “pioneered privacy-preserving methods under Local Differential Privacy and extended privacy frameworks to Riemannian manifolds, safeguarding sensitive data in fields such as medical imaging and health care analytics.”
Of his work as a whole, a writer says that Linglong has “pioneered statistical methods in neuroimaging data analysis that integrate spatial, functional, and high-dimensional data, enabling ground-breaking insights into brain structure and function.” He goes on to say that “Dr. Kong's contributions to trustworthy machine learning address some of the most pressing challenges in AI, including fairness and privacy. His work on conformalized fairness via quantile regression and Gaussian differential privacy on Riemannian manifolds exemplifies his ability to combine rigorous statistical theory with impactful, ethical applications. These contributions are critical for developing AI systems that are equitable, reliable, and aligned with societal values.”
In summary, Linglong has risen to the very top tier of mathematical statisticians and data scientists in this country and internationally.
Linglong and his wife, colleague, and prolific collaborator Bei Jiang have, apart from a host of significant papers, also two sons, Denver and Daylan.