Zhigang Yao

Bio

Zhigang Yao is an associate professor and tenured professor in the Department of Statistics and Data Science at the National University of Singapore. He is currently a visiting member of the Center for Mathematical Sciences and Applications at Harvard University, a visiting professor at YMSC at Tsinghua University, and has also visited universities such as the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland as a Professeur invitée. His research interests include statistical inference of complex data. In recent years, he has focused on research on non-Euclidean statistics and low-dimensional manifold fitting. With the collaboration and help of Professor Shing-Tung Yau, Professor Yao is committed to promoting research in the new field of interaction between geometry and statistics. In recent years, Professor Yao and his collaborators have proposed methods and theories to redefine the principal flow/sub-manifold and principal boundary of traditional PCA on Riemannian manifolds, as well as new methods and theories for manifold fitting in the ambient space. These methods aim to address deficiencies in traditional statistical methods and theories by mining the geometric structures hidden in the data itself. Currently, these methods and theories have been gradually used in the analysis of large-scale data, including single-cell sequencing data and network data. Personal webpage https://zhigang-yao.github.io/

Education Experience

• 2011 University of Pittsburgh Statistics Ph.D

Work Experience

• 2022- Center of Mathematical Sciences and Applications Harvard University, USA Visiting Professor (Member)
• 2022 Swiss Federal Institute of Technology (EPFL), Switzerland Professeur invitée (Visiting Professor)
• 2020- Department of Statistics and Data Science National University of Singapore, Singapore Associate Professor (with Tenure)
• 2020- Department of Mathematics National University of Singapore, Singapore Associate Professor (by Courtesy)
• 2014-2020 Department of Statistics and Data Science National University of Singapore, Singapore Assistant Professor
• 2018- Institute of Data Science National University of Singapore, Singapore Affiliate Faculty Member

Honors and Awards

• Elected Member, International Statistical Institute (ISI), 2018-present

Publications

1. Manifold Fitting, Yao, Z., Su, J., Li, B. and Yau, S.T. (2023) DOI: https://arxiv.org/abs/2304.07680
2. Single-Cell Analysis via Manifold Fitting: A New Framework for RNA Clustering and Beyond, Yao, Z., Li, B., Lu, Y. and Yau, S.T. (2024) (Invited Submission to Proceedings of the National Academy of Sciences of the United States of America, Revised)
3. Manifold Fitting with CycleGAN, Yao, Z., Su, J. and Yau, S.T, Proceedings of the National Academy of Sciences of the United States of America, 2023, 121 (5) e2311436121. [Impact Factor: 12.779]
4. Random Fixed Boundary Flows, Yao, Z., Xia. Y. and Fan Z, Y. Journal of the American Statistical Association, 2023, In press. [Impact Factor: 2.570]
5. Quantifying Time-Varying Sources in Magnetoencephalography — A Discrete Approach, Yao, Z., Fan, Z., Hayashi, M. and Eddy, W.F. Annals of Applied Statistics, 2020, 14, 1379-1408. [Impact Factor: 2.570]
6. Principal Boundary on Riemannian Manifolds, Yao, Z. and Zhang, Z. Journal of the American Statistical Association, 2020, 15, 1435-1448. [Impact Factor: 3.139]
7. Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach, Zhang, Y., Yao, Z., Forssen, P. and Torgny, F. Annals of Applied Statistics, 2019, 13, 2011-2042. [Impact Factor: 2.570]
8. Estimating the number of sources in Magnetoencephalography using spiked population eigenvalues, Yao, Z., Zhang, Y., Bai, Z. and Eddy, W.F, Journal of the American Statistical Association, 2018, 113 505-518. [Impact Factor: 3.139]
9. Partial Correlation Screening for Estimating Large Precision Matrices, with Applications to Classification, Huang, S., Jin J. and Yao, Z. Annals of Statistics, 2016, 44, 2018-2057. [Impact Factor: 2.307]
10. A Statistical Approach to the Inverse Problem in Magnetoencephalography, Yao, Z. and Eddy, W. F. Annals of Applied Statistics 2014, 8, 1119-1144. [Impact Factor: 2.570]
11. Principal Flows, Panaretos, V. M., Pham, T. and Yao, Z. Journal of the American Statistical Association 2014, 109, 424-436. [Impact Factor: 3.139]
12. Optimal Classification in Sparse Gaussian Graphic Model, Fan, Y., Jin, J. and Yao, Z. Annals of Statistics 2013, 41, 2537-2571. [Impact Factor: 2.307]
13. Genovese, C. R., Jin, J., Wasserman, L. and Yao, Z. (2012) A Comparison of the Lasso and Marginal Regression, Journal of Machine Learning Research, 13, 2107-2143. [Impact Factor: 5.177]