Dengdeng Yu

Assistant Professor, Statistics and Data Science

Dengdeng Yu Headshot

Contact

Bio

Obtained BSc in Information and Computing Science from Southeast University (2007), followed by an MSc in Finance from the University of Ulm (2011). He completed an MSc and PhD in Statistics at the University of Alberta (2017) under the supervision of Dr. Linglong Kong and Dr. Ivan Mizera. He held postdoctoral appointments at the University of Toronto and the Canadian Statistical Sciences Institute (CANSSI) (2018–2020) and at the University of Alberta (2020) before joining UT Arlington as an assistant professor of mathematics (2021–2023). Currently, he is an assistant professor in the Department of Management Science and Statistics at the Alvarez College of Business, UT San Antonio. 

His research interests include High Dimensional Data Analysis, Functional Data Analysis, Statistical Machine Learning, Causal Inference, Quantile Regression, Neuroimaging Data Analysis, and Imaging Genetics. To view his up-to-date publications, please visit the Google Scholar profile.

Personal Website 

Teaching

  • Statistical learning and data mining
  • Statisitcal Inference
  • Regression Analysis

Research Interests

  • High Dimensional Data Analysis
  • Functional Data Analysis
  • Nonparametric Data Analysis
  • Statistical Machine Learning
  • Casual Inference

  • Quantile Regression

Degrees

  • PhD Statistics, University of Alberta, Canada
  • MSc Statistics, University of Alberta, Canada
  • MSc Finance, University of Ulm, Germany
  • BSc Information and Computing Science, Southeast University, China

Publications

  • Dengdeng Yu, Matthew Pietrosanu, Ivan Mizera, Bei Jiang, Linglong Kong, Wei Tu (2025). Functional linear partial quantile regression with guaranteed convergence for neuroimaging data analysis. Statistics in Biosciences, 17 (1), 174-190.
  • Dengdeng Yu and Dehan Kong (2025), Nuclear Norm Regularization, Wiley Interdisciplinary Reviews: Computational Statistics, 17 (1), e70013.
  • Yang Hu, Nicole Denier, Lei Ding, Monideepa Tarafdar, Alla Konnikov, Karen D Hughes, Shenggang Hu, Bran Knowles, Enze Shi, Jabir Alshehabi Al-Ani, Irina Rets, Linglong Kong, Dengdeng Yu, Hongsheng Dai, Bei Jiang (2024). Language in job advertisements and the reproduction of labor force gender and racial segregation, PNAS Nexus, 3 (12),  pgae526
  • Dengdeng Yu, Linbo Wang, Dehan Kong and Hongtu Zhu. (2022). Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Studies. Journal of the American Statistical Association, 117 (540), 1656-1668. 
  • Yang Zhou, Mark Koudstall, Dengdeng Yu, Dehan Kong and Fang Yao (2022). Nonparametric Principal Subspace Regression. Journal of Machine Learning Research, 23(237), 1-28. 
  • Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang (2022). Conformalized fairness via quantile regression. Advances in Neural Information Processing Systems, 35, 11561-11572.
  • Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson, Bo Song, An Zhang, Jinfeng Liu, Sile Tao, Jiayi Cheng, Xiaohong Liu, Dengdeng Yu, Matthew Poon and Animesh Garg (2022). Smpl: Simulated industrial manufacturing and process control learning environments. Advances in Neural Information Processing Systems, 35, 26631-26644
  • Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao and Bei Jiang (2022), Word embeddings via causal inference: Gender bias reducing and semantic information preserving, Proceedings of the AAAI Conference on Artificial Intelligence, 36, 11864-11872.
  • Cory R Weissman, Itay Hadas, Dengdeng Yu, Brett Jones, Dehan Kong, Benoit H Mulsant, Daniel M Blumberger and Zafiris J Daskalakis, Predictors of change in suicidal ideation across treatment phases of major depressive disorder: analysis of the STAR*D data, Neuropsychopharmacology, 46 (7), 1293-1299.
  • Xiucai Ding, Dengdeng Yu, Zhengwu Zhang and Dehan Kong (2021), Multivariate functional response low‐rank regression with an application to brain imaging data, Canadian Journal of Statistics 49, (1), 150-181.
  • Dengdeng Yu, Li Zhang, Ivan Mizera, Bei Jiang, Linglong Kong (2019), Sparse wavelet estimation in quantile regression with multiple functional predictors, Computational statistics & data analysis, 136, 12-29.
  • Dengdeng Yu, Linglong Kong and Ivan Mizera (2016), Partial functional linear quantile regression for neuroimaging data analysis, Neurocomputing, 195, 74-87.