Skip to main content

Maggie Niu

Associate Professor, Director of the Statistical Consulting Center
Maggie Niu


Xiaoyue Maggie Niu is an Associate Professor of Statistics and Director of the Statistical Consulting Center at Penn State.

Niu received her Ph.D. in Statistics from University of Washington in 2010. She received her M.S. in Applied Math from University of Minnesota Duluth in 2005, and a B.S. in Applied Math from Peking University in 2003.

Her research focuses on the development of statistical models that solve real world problems, especially with applications in health and social sciences. The methodological approaches she takes include Bayesian methods, social network models, and latent variable models. Another big part of her work is in the statistical consulting center. She collaborates with a variety of researchers on campus and mentor graduate students to work with them. Solving practically important problems and interacting with people from diverse background are the most enjoyable part of her work.

Niu has contributed to the professional society as the chair of the ASA Section on Statistical Consulting and JSM poster chair. She is currently serving as an Associate Editor for JASA, AOAS and Stat, and co-editor for Stat's Special issue on "Statistical Consulting and Collaboration". She joined Penn State as an assistant research professor in 2011.


Selected Publications

  • J. Wang, X. Cai, X. Niu, R. Li, “Variable Selection for High-dimensional Nodal Attributes in Social Networks with Degree Heterogeneity”, Journal of the American Statistical Association, accepted, 2023+
  • B. Kim, X. Niu, D. Hunter, and X. Cao, “A Dynamic Additive and Multiplicative Effects Network Model for United Nations Voting”, Annals of Applied Statistics, accepted, 2023+
  • I. Laga, L. Bao, X. Niu, “A Correlated Network Scale-up Model: Finding the Connection Between Subpopulations”, Journal of the American Statistical Association, accepted, 2023+
  • X. Niu, “Learning about an Academic Statistical Consulting Center through Data”, Stat, 12(1), 2023
  • L. Bao, X. Niu, M. Mahy, P. D. Ghys, “Estimating HIV Epidemics for Sub-national Areas”, Annals of Applied Statistics, accepted, 2022+
  • Y. Tao, D. Li, X. Niu, “Grouped Network Poisson Autoregressive Model”, Statistica Sinica, accepted, 2022+
  • I. Laga1, X. Niu1, K. Rucinski, S. Baral, D. Chen, N. Viswasam, K. Sabin, J. Zhao, J. W. Eaton, L. Bao, “Mapping the population size of female sex worker in countries across sub-Saharan Africa”, Proceedings of the National Academy of Sciences, 120(2), 2022
  • I. Laga, X. Niu, L. Bao, “Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi”, Journal of the American Statistical Association, 117(537), 2022

  • J. Hehir, A. Slakovic, X. Niu, “Consistent Spectral Clustering of Network Block Models under Local Differential Privacy”, Journal of Privacy and Confidentiality, 12(2), 2022
  • L. Bao1, X. Niu1, Y. Zhang1, “What We Can Learn from the Exported Cases in Detecting Disease Outbreaks – A Case Study of the COVID-19 Epidemic”, Annals of Epidemiology, 75, 67-72, 2022
  • J. Parsons, L. Bao, and X. Niu, “A Bayesian hierarchical modeling approach to combining multiple data sources: A case study in population size estimation”, Annals of Applied Statistics, accepted, 2021+

  • X. Cui, F. Zhou, P. Ciais, E. A. Davidson, J. G. Canadell, X. Niu, F. N. Tubiello, X. Ju, A. F. Bouwman, R. B. Jackson, N. D. Mueller, X. Zheng, D. Kanter, H. Tian, W. Adalibieke, Y. Bo, Q. Wang, X. Zhan, D. Zhu, “Global Hotspots of Nitrous Oxide Mitigation Potential”, Nature Food, 2, 886–893, 2021

  • I. Laga, L. Bao, X. Niu, “Thirty Years of the Network Scale-up Method”, Journal of the American Statistical Association, 116(535), 1548-1559, 2021

  • X. Niu, A. Rao, D. Chen, B. Sheng, S. Weir, E. Umar, G. Trapence, V. Jumbe, D. Kamba, K. Rucinski, N. Viswasam, S. Baral, L. Bao, “Using factor analyses to estimate the number of female sex workers across Malawi from multiple regional sources”, Annals of Epidemiology, 55, 34-40, 2021

  • X. Niu, and P.D. Hoff. Joint Mean and Covariance Modeling of Multiple Health Outcomes, Annals of Applied Statistics, 13 (1), 321-339, 2019

  • X. Niu and J.L. Rosenberger. Near-Balanced Incomplete Block Designs, with An Application to Poster Competitions, The American Statistician, 73(2), 159-164, 2019

  • B. Kim, K. Lee, L. Xue, and X. Niu. A Survey of Dynamic Network Models with Latent Variables, Statistics Surveys, 12(0), 105-135, 2018

  • X. Niu, A. Zhang, T. Brown, R. Puckett, M. Mahy, and L. Bao. Incorporation of Hierarchical Structure into EPP Fitting with Examples of Estimating Sub-National HIV/AIDS Dynamics, AIDS,31: S51-S59, 2017

  • P.D. Hoff, X. Niu, and J.A. Wellner. Information bounds for Gaussian copulas, Bernoulli, 20(2):604–622, 2014

  • P.D. Hoff and X. Niu. A Covariance Regression Model, Statistica Sinica, 22(2): 729-753, 2012

  • A.E. Raftery, X. Niu, P.D. Hoff, and K.Y. Young. Fast Inference for Latent Space Network Models using Case-control Likelihoods, Journal of Computational and Graphical Statistics. 21:901–919, 2012



STAT 580/581 - Statistical Consulting Practicum I/II

STAT 597 - Statistical Analysis of Network Data