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Key Insights into Data Analytics

Big data and machine learning may be buzzwords in popular culture, but Yingying Fan has been contributing key insights into data analytics for more than a decade.

February 19, 2020
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“We develop mathematically rigorous, computationally efficient, and interpretable approaches to dealing with highly complex big data with reproducible inference.”

 

Big data and machine learning may be buzzwords in popular culture, but Yingying Fan has been contributing key insights into data analytics for more than a decade. Her work in big data, data science, and machine learning has advanced developments in high-dimensional statistical inference, classification and variable selection, nonparametric statistics, and financial econometrics.

In 2018, Fan was awarded a multi-year grant to develop methods that leverage big data to produce interpretable and reproducible results, with applications in biomedical and health science problems. These methods are intended to extend her previous paper, “Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection” in the Journal of the Royal Statistical Society, to complex models, such as deep learning models to enhance their interpretability and reproducibility, two increasingly important issues in both academia and industry. 

Since winning the award, Fan and her team have had two new papers accepted for publication in the Journal of the American Statistical Association: “RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs” and “IPAD: Stable Interpretable Forecasting with Knockoffs Inference.

Fan joined the Department of Data Sciences and Operations as assistant professor in 2009, after serving as a lecturer in the Department of Statistics at Harvard and as a visiting fellow in DSO. She was named Associate Professor of DSO in 2015, as well as Associate Professor of both the USC Dornsife Department of Economics and the Department of Computer Science in the Viterbi School of Engineering. In 2017, Fan was named Associate Fellow in the Institute for New Economic Thinking, and in 2018, Dean’s Associate Professor in Business Administration. In 2019, Fan was named Professor of Data Sciences and Operations. That same year she was inducted as a Fellow of American Statistical Association (ASA).

Fan has been serving as associate editor of numerous academic journals since 2012, including Journal of the American Statistical Association, Journal of Econometrics, Journal of Business & Economic Statistics, and The Econometrics Journal.

Among her many awards, Fan received the NSF Faculty Early Career Development Award (CAREER) in 2012, which is the highest distinction that the National Science Foundation can provide to junior researchers, given to just five statisticians across the nation that year. In 2013 Fan received the prestigious Noether Young Scholar Award, which is given annually to a single accomplished young researcher in statistics.

In 2017 Fan received the Royal Statistical Society (RSS) Guy Medal in Bronze, the most prestigious award given to young statisticians. She is the first female American citizen to win the distinction. 

Fan earned her M.S. and Ph.D. in operations research and financial engineering from Princeton University, and her B.S. in statistics and finance from the University of Science and Technology of China.