By Thomas Jilk
Wing Hung Wong PhD ‘80, one of the world’s premier scholars in statistics and bioinformatics, has received an inaugural Distinguished Achievement Award from the School of Computer, Data & Information Sciences (CDIS). Wong, a luminary in statistics and biomedical data science, has served on the faculty at Stanford University since 2004, where he remains a Professor of Statistics and Biomedical Data Science and holds the Stephen R. Pierce Family Goldman Sachs Professorship in Science & Human Health. This prestigious recognition acknowledges not only Wong’s exceptional contributions to the field but also the far-reaching impact of his work in transforming biomedical data science.
One of the leading researchers at the intersection of statistics and computational biology, Wong credits his time at UW–Madison with instilling fundamental approaches to his field that have served him well throughout his pathbreaking career.
“Professionally, Madison is where I started my career as a statistician, and personally, it’s where I met my wife,” Wong said.
“This recognition, from a place that shaped so much of my professional and personal life, really means the world to me.”
Wing Wong
Pioneering research
Since the 1980s, Wong has held positions in several of the world’s most prestigious academic institutions and top statistics departments. After earning his doctoral degree from UW–Madison, Wong joined the University of Chicago Department of Statistics, rising from assistant professor to full professor and later holding faculty roles at the Chinese University of Hong Kong, UCLA and Harvard. For the last 20 years, Wong has led groundbreaking research in Stanford’s Department of Statistics, where his work in statistical genomics, particularly through the Bio-X interdisciplinary biosciences institute, has shaped the trajectory of modern biomedical research.
A central question propelling Wong’s research, he said, is “How do we get from genotype to phenotype?” More precisely, how can researchers harness advanced statistical tools, and what Wong called “really big data,” to decode the influence of genes on human health. His pioneering work addresses this fundamental issue by drawing on vast datasets to bridge the gap between genomic data and clinical insights.
“In the not-too-far future, you can imagine a world where every person would have their whole genome sequenced,” Wong said, and everyone would also have an electronic medical record, a detailed record of lab tests and diagnosis and drug history. So, you have these two types of big data on genotype and phenotype, at a population scale.” Such a scenario, Wong noted, would bring enormous possibilities for personalized medicine.
With such large datasets, Wong said, the temptation can be to lean on machine learning models to crunch the numbers and calculate how a gene affects a physical trait or a susceptibility to disease—how a genetic variable affects a physical one. However, he emphasized, “the problem is more complicated than that, because biology and medicine are domains with deep scientific knowledge, and we don’t want to just treat it as a black-box machine learning problem” that fails to capture the nuances of the rich scientific frameworks of biology and medicine. Instead, Wong focuses on the “intermediate layers of knowledge between genotype and phenotype”, studying the dynamic interactions of gene expression and cellular processes that define individual health outcomes.
The challenge for statisticians, then, is to “integrate all this information from all these types of data together to answer this question of how we go from genotype to phenotype,” Wong said. “That’s my long-term research goal.”
Wong’s innovative approaches in bioinformatics, including statistical methods for analyzing gene expression and gene regulatory networks, have profoundly influenced the field. His work has been cited thousands of times, underscoring his legacy as a driving force in propelling the research enterprise forward in these areas.
In recognition of his pioneering contributions, Wong has previously been honored with the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award (1993) and the COPSS Distinguished Achievement Award (2021). He was also elected to the US National Academy of Sciences in 2009.
Shaping the future
Wong credited two transformative figures in the UW–Madison Department of Statistics, former Professors Grace Wahba and George Box, both titans in the field, for impacting his thinking and setting him up for a trailblazing career. “Grace Wahba was my advisor … she taught me the importance of computing, and she continued to check in with me even long after graduation,” he said.
Box, Wong said, was also a memorable instructor and renowned scholar in Bayesian statistics, a foundational concept which Wong has relied on his entire career. (Box founded the UW–Madison Department of Statistics in 1960.)
As for Wong’s advice for future statisticians and data scientists: pursue genuine curiosity over trends. “Interest trumps everything else. If you’re really interested, even when the problem is difficult, you will make progress.”
He added, “Always consider the science behind the data, because each dataset comes with its own scientific background.” And for young academics, Wong offered some specific advice: “Try hard to recruit good graduate students to work with you, because it’s always more fun to work with students rather than struggling by yourself.”
Wong’s own legacy in mentorship is remarkable; his graduate students have gone on to hold faculty positions at elite institutions like Harvard, Stanford, the University of Chicago and UW–Madison. In fact, according to North Dakota State University’s Mathematics Genealogy Project, a total of 270 of Wong’s academic “descendants” now serve at universities across the country and world, shaping the future of statistics and data science.
Congratulations to Professor Wing Hung Wong on this distinguished recognition, honoring his lifetime of achievements and enduring contributions to statistics and data science.