Preprints and Working Papers

  1. Optimizing probabilistic conformal prediction with vectorized non-conformity scores.
    M. Zheng and S. Zhu. 
  2. Recurrent neural goodness-of-fit test for time series.
    A. Zhang, W. Zhou, L. Xie, and S. Zhu. 
  3. Balancing optimality and diversity: Human-centered decision-making through generative curation. [Code]
    M. L. Li and S. Zhu. Submitted. 
  4. New user event prediction through the lens of causal inference.
    H. S. Yuchi, S. Zhu, L. Dong, Y. M. Arisoy, and M. C. Spencer. 
  5. TimeAutoDiff: Combining autoencoder and diffusion model for time series tabular data synthesizing.
    N. Suh, Y. Yang, D. Hsieh, Q. Luan, S. Xu, S. Zhu, and G. Cheng. 
  6. Counterfactual fairness through transforming data orthogonal to bias.
    S. Chen and S. Zhu. 
  7. Conditional generative representation for black-box optimization with implicit constraints. [Poster]
    W. Xing, J. Lee, C. Liu, and S. Zhu.
  8. Conditional generative modeling for high-dimensional marked temporal point processes. [Code]
    Z. Dong, Z. Fan, and S. Zhu.
  9. Generalized Hypercube queuing models with overlapping service regions. [Code]
    S. Zhu, W. Xing, and Y. Xie. Under Review. 
  10. Quantifying grid resilience against extreme weather using large-scale customer power outage data. [Poster] [Code]
    S. Zhu, R. Yao, Y. Xie, F. Qiu, Y. Qiu, and X. Wu. In Minor Revision. 
  11. Multi-resolution spatio-temporal prediction with application to wind power generation. [Slides] [Poster]
    Z. Dong, H. Zhang, S. Zhu, Y. Xie, and P. Van Hentenryck. Submitted. 

Journal

  1. Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data. [arXiv] [Poster] [Code]
    Z. Dong, S. Zhu, Y. Xie, J. Mateu, and F. J. Rodríguez-Cortés.
    Journal of the Royal Statistical Society, Series C. Vol. 72, Issue 2, Pages 368–386, May 2023.
  2. Sequential adversarial anomaly detection for one-class event data. [arXiv] [Slides] [Code]
    S. Zhu, H. S. Yuchi, M. Zhang, and Y. Xie.
    INFORMS Journal on Data Science. Vol 2, No. 1, Pages 45-59, Mar 2023.
  3. Data-driven optimization for Atlanta police zone design. [arXiv] [Slides] [Code]
    S. Zhu, H. Wang, and Y. Xie.
    INFORMS Journal on Applied Analytics (formerly Interfaces). Vol. 52, Issue 5, Pages 412-432, Oct 2022.
  4. Spatio-temporal-textual point processes for crime linkage detection. [arXiv] [Poster] [Code]
    S. Zhu and Y. Xie.
    Annals of Applied Statistics. Vol. 16, No. 2, Pages 1151-1170, June 2022.
  5. Early detection of COVID-19 hotspots using spatio-temporal data. [arXiv] [Slides] [Poster] [Code]
    S. Zhu, A. Bukharin, L. Xie, K. Yamin, S. Yang, P. Keskinocak, and Y. Xie.
    IEEE Journal of Selected Topics in Signal Processing. Vol. 16, Issue 2, Page 250-260, Feb 2022.
  6. Imitation learning of neural spatio-temporal point processes. [arXiv] [Slides] [Poster] [Code]
    S. Zhu, S. Li, Z. Peng, and Y. Xie.
    IEEE Transactions on Knowledge and Data Engineering. Vol. 34, Issue 11, Nov 2022.
  7. Spatio-temporal point processes with attention for traffic congestion event modeling. [arXiv] [Code]
    S. Zhu, R. Ding, M. Zhang, P. Van Hentenryck, and Y. Xie.
    IEEE Transactions on Intelligent Transportation Systems. Vol. 23, Issue 7, July 2022.
  8. In Situ Transmission Electron Microscopy: Signal processing challenges and example. [arXiv]
    J. Kacher, Y. Xie, S. P. Voigt, S. Zhu, H. S. Yuchi, J. Key, and S. R. Kalidindi.
    IEEE Signal Processing Magazine (Survey paper). Vol. 39, Issue 1, Jan 2022.
  9. High-resolution spatio-temporal model for county-level COVID-19 activity in the U.S. [arXiv] [Code]
    S. Zhu, A. Bukharin, L. Xie, M. Santillana, S. Yang, and Y. Xie.
    ACM Transactions on Management Information Systems. Vol. 12, Issue 4, Article 33, Dec 2021.
  10. Investigating local oxidation processes in Fe thin films in a water vapor environment by in situ liquid cell TEM.
    J. Key, S. Zhu, C. M. Rouleauc, R. R. Unocic, Y. Xie, and J. Kacher.
    Ultramicroscopy. Vol. 209, Feb 2020.

Conference

  1. Counterfactual generative models for time-varying treatments. [arXiv] [Slides] [Poster] [Code]
    S. Wu, W. Zhou, M. Chen, and S. Zhu.
    ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024.
  2. Distributionally robust weighted k-nearest neighbors. [arXiv] [Poster] [Code] [News]
    S. Zhu, L. Xie, M. Zhang, R. Gao, and Y. Xie.
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
  3. Neural spectral marked point processes. [arXiv] [Slides] [Poster] [Code] [News]
    S. Zhu, H. Wang, Z. Dong, X. Cheng, and Y. Xie.
    International Conference on Learning Representations (ICLR), 2022.
  4. Sequential adversarial anomaly detection with deep Fourier kernel. [Slides]
    S. Zhu, H. S. Yuchi, M. Zhang, and Y. Xie.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
  5. Deep Fourier kernel for self-attentive point processes. [arXiv] [Slides]
    S. Zhu, M. Zhang, R. Ding, and Y. Xie.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
    • Oral presentation, acceptance rate: 48/1527 = 3.1%.
  6. Goodness-of-fit test for self-exciting processes. [arXiv]
    S. Wei, S. Zhu, M. Zhang, and Y. Xie.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  7. Adversarial anomaly detection for marked spatio-temporal streaming data. [Slides] [Poster] [Code]
    S. Zhu, H. S. Yuchi, and Y. Xie.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.
  8. Crime event embedding with unsupervised feature selection. [arXiv] [Poster] [Code]
    S. Zhu and Y. Xie.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.
  9. Learning temporal point processes via reinforcement learning. [arXiv] [Code]
    S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song.
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2018.
    • Spotlight, acceptance rate: 168/4856 = 3.5%.
  10. Crime incidents embedding using restricted Boltzmann machines. [arXiv] [Code]
    S. Zhu and Y. Xie.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  11. Sequential adaptive detection for in-situ transmission electron microscopy (TEM). [arXiv] [Poster]
    Y. Cao, S. Zhu, Y. Xie, J. Key, J. Kacher, R. R. Unocic, and C. M. Rouleau.
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Workshop

  1. Assessing electricity service unfairness with transfer counterfactual learning.
    S. Wei, X. Kong, A. Santos Xavier, S. Zhu, Y. Xie, and F. Qiu.
    Causal Representation Learning Workshop at NeurIPS 2023. 
  2. Uncertainty-aware robust learning on noisy graphs. [Poster] [Code]
    S. Chen, K. Ding, and S. Zhu.
    New Frontiers in Graph Learning Workshop at NeurIPS 2023. 
  3. Data-driven optimization for police beat design in South Fulton, Georgia. [arXiv] [Slides]
    S. Zhu, A. Bukharin, L. Lu, H. Wang, and Y. Xie.
    Data Science for Social Good at KDD 2021. 
    • The proposed beat design was adopted by South Fulton Police Department and implemented in 2020.

Patent

  1. Methods and systems for data analysis by text embeddings.
Y. Xie and S. Zhu. U.S. Patent No. US20190318223A1.

Thesis

  1. Statistical Learning and Decision Making for Spatio-Temporal Data. 
    Ph.D. dissertation, Georgia Institute of Technology. April 2022. 

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