Publications


Google Scholar


Pre-prints

  1. Chi, J. T., & Needell, D. (2022). Sketched Gaussian Model Linear Discriminant Analysis via the Randomized Kaczmarz Method. arXiv:2211.05749 [stat.CO].


Peer-Reviewed Publications

  1. Ding, X., Dong, X., McGough, O., Shen, C., Ulichney, A., Xu, R., Swartworth, W., Chi, J. T., & Needell, D. (2022). Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data. Proceedings of the IEEE/ACM International Conference on Big Data Computing, Applications and Technologies: National Symposium for NSF REU Research in Data Science, Systems, and Security, Portland, Oregon, USA, December 6–9, 2022, Accepted.
  2. Chi, J. T., & Chi, E. C. (2022). A User-Friendly Computational Framework for Robust Structured Regression with the L_2 Criterion. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2022.2035232
  3. Chi, J. T., & Ipsen, I. C. F. (2022). A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression. Information and Inference, 11(3), 1055–1077. https://doi.org/10.1093/imaiai/iaab016
  4. Chi, J. T., Ipsen, I. C. F., Hsiao, T.-H., Lin, C.-H., Wang, L.-S., Lee, W.-P., Lu, T.-P., & Tzeng, J.-Y. (2021). SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based Gene-Environment Interaction Tests in Biobank Data. Frontiers in Genetics, Section Statistical Genetics and Methodology, 12, 1878. https://doi.org/10.3389/fgene.2021.710055
  5. Chi, J. T., & Ipsen, I. C. F. (2021). Multiplicative Perturbation Bounds for Multivariate Multiple Linear Regression in Schatten p-Norms. Linear Algebra and Its Applications, 624, 87–102. https://doi.org/10.1016/j.laa.2021.03.039
  6. Qin, J., Lee, H., Chi, J. T., Drumetz, L., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2021). Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 3338–3351. https://doi.org/10.1109/TGRS.2020.3020810
  7. Qin, J., Lee, H., Chi, J. T., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2019). Fast Blind Hyperspectral Unmixing based on Graph Laplacian. 2019 IEEE Workshop on Hyperspectral Imaging and Signal Processing (WHISPERS).
  8. Chi, J. T., Chi, E. C., & Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. The American Statistician, 70(1), 91–99. https://doi.org/10.1080/00031305.2015.1086685
  9. Chi, J. T., & Handcock, M. S. (2014). Identifying Sources of Healthcare Underutilization Among California’s Immigrants. Journal of Racial and Ethnic Health Disparities, 1(3), 207–218. https://doi.org/10.1007/s40615-014-0028-0


Unrefereed Articles

  1. Chi, J. T., & Chi, E. C. (2014). Getting to the Bottom of Matrix Completion and Nonnegative Least Squares with the MM Algorithm. StatisticsViews.com.
  2. Chi, J. T., & Chi, E. C. (2014). Getting to the Bottom of Regression with Gradient Descent. StatisticsViews.com.