Chi, E. C., Molstad, A. J., Gao, Z., & Chi, J. T. (2025). The Why and How of Convex Clustering, Accepted at Annual Review of Statistics and Its Application (ARSIA).
@article{chi2025convexclustering,
title = {The Why and How of Convex Clustering, \emph{Accepted at Annual Review of Statistics and Its Application (ARSIA)}},
author = {Chi, Eric C. and Molstad, Aaron J. and Gao, Zheming and Chi, Jocelyn T.},
year = {2025},
arxiv = {https://arxiv.org/abs/2507.09077},
link = {https://arxiv.org/abs/2507.09077},
pdf = {https://arxiv.org/abs/2507.09077.pdf},
howpublished = {arXiv: 2507.09077 [stat.ME]},
group = {publications}
}
Ghorbannia, A., Tanade, C., Yousef, A., Khan, N., Vardhan, M., Chi, J. T., Das, A., Leopold, J., Chi, E. C., & Randles, A. (2025). Physics-Based Machine Learning for Real-Time Assessment of Side-Branch Hemodynamics in Coronary Bifurcation Lesions. International Journal of High Performance Computing Applications, 39(5), 678—691. https://doi.org/10.1177/10943420251351125
@article{bifurcation,
author = {Ghorbannia, Arash and Tanade, Cyrus and Yousef, Ayman and Khan, Nusrat and Vardhan, Madhurima and Chi, Jocelyn T. and Das, Arpita and Leopold, Jane and Chi, Eric C. and Randles, Amanda},
title = {Physics-Based Machine Learning for Real-Time Assessment of Side-Branch Hemodynamics in Coronary Bifurcation Lesions},
group = {publications},
volume = {39},
number = {5},
pages = {678—691},
year = {2025},
journal = {International Journal of High Performance Computing Applications},
link = {https://journals.sagepub.com/doi/10.1177/10943420251351125},
doi = {10.1177/10943420251351125}
}
Chi, J. T., & Needell, D. (2025). Linear Discriminant Analysis with the Randomized Kaczmarz Method. SIAM Journal on Matrix Analysis and Applications, 46(1), 94–120. https://doi.org/10.1137/23M155493X
@article{chineedell,
author = {Jocelyn T. Chi and Deanna Needell},
title = {Linear Discriminant Analysis with the Randomized {K}aczmarz Method},
group = {publications},
year = {2025},
arxiv = {https://arxiv.org/abs/2211.05749},
journal = {SIAM Journal on Matrix Analysis and Applications},
pdf = {https://arxiv.org/pdf/2211.05749.pdf},
doi = {10.1137/23M155493X},
link = {https://epubs.siam.org/doi/10.1137/23M155493X},
volume = {46},
issue = {1},
pages = {94-120}
}
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. https://doi.org/10.1109/BDCAT56447.2022.00035
@inproceedings{phnmf,
author = {Xiaofu Ding and Xinyu Dong and Olivia McGough and Chenxin Shen and Annie Ulichney and Ruiyao Xu and William Swartworth and Jocelyn T. Chi and Deanna Needell},
title = {Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data},
booktitle = {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},
series = {BDCAT 2022},
year = {2022},
publisher = {IEEE Computer Society},
group = {publications},
arxiv = {https://arxiv.org/abs/2209.04968},
pdf = {https://arxiv.org/pdf/2209.04968.pdf},
doi = {10.1109/BDCAT56447.2022.00035},
link = {https://ieeexplore.ieee.org/abstract/document/10061915/}
}
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, 31(4), 1051–1062. https://doi.org/10.1080/10618600.2022.2035232
@article{l2e,
author = {Jocelyn T. Chi and Eric C. Chi},
title = {A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion},
year = {2022},
group = {publications},
journal = {Journal of Computational and Graphical Statistics},
code = {https://jocelynchi.github.io/L2E-package-demo/},
arxiv = {https://arxiv.org/abs/2010.04133},
pdf = {https://arxiv.org/pdf/2010.04133.pdf},
doi = {10.1080/10618600.2022.2035232},
link = {https://www.tandfonline.com/doi/full/10.1080/10618600.2022.2035232},
volume = {31},
number = {4},
pages = {1051-1062},
publisher = {Taylor & Francis}
}
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
@article{ChiIpsenRandLS,
author = {Jocelyn T. Chi and Ilse C. F. Ipsen},
title = {A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression},
year = {2022},
group = {publications},
arxiv = {https://arxiv.org/abs/1808.05924},
journal = {Information and Inference},
volume = {11},
issue = {3},
pages = {1055--1077},
doi = {10.1093/imaiai/iaab016},
link = {https://academic.oup.com/imaiai/article-abstract/11/3/1055/6347858}
}
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
@article{seagle,
author = {Jocelyn T. Chi and Ilse C. F. Ipsen and Tzu-Hung Hsiao and Ching-Heng Lin and Li-San Wang and Wan-Ping Lee and Tzu-Pin Lu and Jung-Ying Tzeng},
title = {SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based Gene-Environment Interaction Tests in Biobank Data},
year = {2021},
group = {publications},
arxiv = {https://arxiv.org/abs/2105.03228},
journal = {Frontiers in Genetics, section Statistical Genetics and Methodology},
volume = {12},
pages = {1878},
pdf = {https://www.frontiersin.org/articles/10.3389/fgene.2021.710055/pdf},
code = {http://jocelynchi.com/SEAGLE},
doi = {10.3389/fgene.2021.710055},
link = {https://www.frontiersin.org/articles/10.3389/fgene.2021.710055/full}
}
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
@article{ChiIpsenMMLR,
author = {Jocelyn T. Chi and Ilse C. F. Ipsen},
title = {Multiplicative Perturbation Bounds for Multivariate Multiple Linear Regression in {S}chatten $p$-Norms},
journal = {Linear Algebra and Its Applications},
year = {2021},
group = {publications},
pages = {87-102},
volume = {624},
arxiv = {https://arxiv.org/abs/2007.06099},
doi = {10.1016/j.laa.2021.03.039},
link = {https://www.sciencedirect.com/science/article/pii/S0024379521001518}
}
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
@article{qinetal2021,
author = {Jing Qin and Harlin Lee and Jocelyn T. Chi and Lucas Drumetz and Jocelyn Chanussot and Yifei Lou and Andrea L. Bertozzi},
title = {Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization},
year = {2021},
volume = {59},
issue = {4},
group = {publications},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
pages = {3338-3351},
doi = {10.1109/TGRS.2020.3020810},
pdf = {http://jocelynchi.com/pdf/hyperspectral_final.pdf}
}
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).
@article{whispers2019,
author = {Jing Qin and Harlin Lee and Jocelyn T. Chi and Jocelyn Chanussot and Yifei Lou and Andrea L. Bertozzi},
title = {Fast Blind Hyperspectral Unmixing based on Graph Laplacian},
journal = {2019 IEEE Workshop on Hyperspectral Imaging and Signal Processing (WHISPERS)},
year = {2019},
group = {publications},
pdf = {http://jocelynchi.com/pdf/cam19-46.pdf}
}
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
@article{ChiChiBaraniuk2016,
author = {Jocelyn T. Chi and Eric C. Chi and Richard G. Baraniuk},
title = {$k$-POD: A Method for $k$-Means Clustering of Missing Data},
journal = {The American Statistician},
year = {2016},
volume = {70},
pages = {91-99},
doi = {10.1080/00031305.2015.1086685},
issue = {1},
group = {publications},
link = {http://www.tandfonline.com/doi/abs/10.1080/00031305.2015.1086685},
arxiv = {http://arxiv.org/abs/1411.7013},
code = {http://jocelynchi.com/kpodclustr}
}
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
@article{ChiHandcock2014,
author = {Jocelyn T. Chi and Mark S. Handcock},
title = {Identifying Sources of Healthcare Underutilization Among California's Immigrants},
journal = {Journal of Racial and Ethnic Health Disparities},
year = {2014},
volume = {1},
pages = {207-218},
doi = {10.1007/s40615-014-0028-0},
issue = {3},
group = {publications},
pdf = {http://link.springer.com/content/pdf/10.1007%2Fs40615-014-0028-0.pdf},
link = {https://link.springer.com/article/10.1007/s40615-014-0028-0}
}