姚志刚

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Research Fields: Interface between Statistics and Geometry Non-Euclidean Statistics High-dimensional Statistical Inference
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简历

姚志刚,新加坡国立大学统计与数据科学系副教授兼终身教授。现为哈佛大学数学科学与应用中心访问成员,清华大学YMSC访问教授,也曾作为特邀客座教授访问瑞士洛桑联邦理工大学 (EPFL) 等大学。研究兴趣主要为复杂数据的统计推断。近年来专注于非欧式统计 (Non-Euclidean Statistics) 和低维流形拟合 (Manifold Fitting) 的研究。姚教授在与丘成桐教授的合作和帮助下,致力于推动几何与统计的交互这一全新领域的研究。近年来,姚教授与其合作者提出在黎曼流形上重新定义传统PCA的principal flow/sub-manifold以及principal boundary等方法和理论,以及全空间下流形拟合的新方法和理论。这些方法通过挖掘隐藏在数据本身的几何结构,旨在解决传统统计方法和理论中的缺陷。目前,这些方法和理论已逐渐被用于大规模数据的分析包括单细胞测序数据和网络数据等。个人网页 https://zhigang-yao.github.io/ https://zhigang-yao.github.io/

教育经历

  • 2011 University of Pittsburgh Statistics 博士

工作经历

  • 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 助理教授
  • 2018- Institute of Data Science National University of Singapore, Singapore Affiliate Faculty Member

荣誉和获奖

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

论著

  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]
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