Preprints and Working Papers

• Topology-aware conformal prediction for stream networks. (Code)
  J. Zhang, F. Wang, P. S. Yu, K. Ding, and S. Zhu.

• Global-decision-focused neural ODEs for proactive grid resilience management.
  S. Chen, F. Fioretto, F. Qiu, and S. Zhu. Submitted to IEEE Transactions on Smart Grid.

• Gen-DFL: Decision-focused generative learning for robust decision making.
  P. Z. Wang, J. Liang, S. Chen, F. Fioretto, and S. Zhu.

• Sequential change point detection via denoising score matching. (Code)
  W. Zhou, L. Xie, Z. Peng, and S. Zhu.

• Generative conformal prediction with vectorized non-conformity scores. (Code)
  M. Zheng and S. Zhu.

• Balancing optimality and diversity: Human-centered decision-making through generative curation. (Code)
  M. L. Li and S. Zhu.

• Counterfactual fairness through transforming data orthogonal to bias.
  S. Chen and S. Zhu.

• Generalized Hypercube queuing models with overlapping service regions. (Code)
  S. Zhu, W. Xing, and Y. Xie. Major Revision at Transportation Science.

• 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. To be submitted.
  Finalist of 2023 QSR Best Student Paper Competition, Awarded to Student Author (Zheng Dong)
  Finalist of Best Applied Paper Competition at 2023 INFORMS DMDA Workshop
  Best Student Paper Nominee & full presentation at 2022 INFORMS Workshop on Data Science
  

2025

• Conditional generative modeling for high-dimensional marked temporal point processes. (arXiv, Code)
  Z. Dong, Z. Fan, and S. Zhu.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.
  A short version is accepted by NeurIPS 2023 Workshop on SyntheticData4ML.

• Hierarchical spatio-temporal uncertainty quantification for distributed energy adoption.
  W. Zhou, S. Zhu, F. Qiu, and X. Wu.
  IEEE Power & Energy Society General Meeting (PESGM), 2025.

• Recurrent neural goodness-of-fit test for time series. (Poster, Code)
  A. Zhang, W. Zhou, L. Xie, and S. Zhu.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

• New user event prediction through the lens of causal inference.
  H. S. Yuchi, S. Zhu, L. Dong, Y. M. Arisoy, and M. C. Spencer.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

• Uncertainty-aware robust learning on noisy graphs. (arXiv, Poster, Code)
  S. Chen, K. Ding, and S. Zhu.
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.

• Black-box optimization with implicit constraints for public policy. (arXiv, Poster, Code)
  W. Xing, J. Lee, C. Liu, and S. Zhu.
  The AAAI Conference on Artificial Intelligence (AAAI), 2025.
  A short version is accepted as a referred paper for 2024 INFORMS Optimization Society Conference.
  Oral presentation (Special Track on AI for Social Impact), acceptance rate: 23/469 = 4.9%
  

2023 - 2024

• 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.
  INFORMS Journal on Data Science. Accepted, Dec 2024.

• 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.
  A short version is accepted by Causal Representation Learning Workshop at NeurIPS 2023.
  Spotlight, DGM4H Workshop at NeurIPS 2023 (Five out of 44 accepted papers)
  Winner, 2023 YinzOR Poster Competition, Awarded to Student Author (Wenbin Zhou)
  

• 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.
  Accepted for presentation at 2022 INFORMS Workshop on Data Science.
  Winner, 2022 INFORMS Data Mining Best Applied Paper
  Runner up, 2022 INFORMS Poster Competition
  

• 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.
  Runner up, Best Applied Paper Competition at 2020 INFORMS DMDA Workshop
  

2022 and Before

• 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.

• 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.
  Finalist of 2021 INFORMS Wagner Prize
  2nd place in 2019 INFORMS Doing Good with Good OR Best Paper Competition
  The proposed zone design was adopted by Atlanta Police Department and implemented in 2019
  

• 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.
  Selected to be presented in The Best of AOAS Session at JSM 2022 (Three out of all papers)
  Best Poster Presentation Award at 2018 Forecasting from Complexity Workshop
  Best Student Poster Award at Georgia Statistics Day 2018
  The project won the 2018 Smart 50 Award at the Smart Cities Connect Conference & Expo
  The algorithm was implemented by Atlanta Police Department in their AWARE system in 2018
  

• 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.

• 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.
  Finalist of Best Applied Paper Competition at 2021 INFORMS DMDA Workshop
  Best Paper Award (Honorable Mention) at ICML Time Series Workshop 2021
  Excellent Poster Award at Georgia Statistics Day 2021
  

• 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.
  A short version is accepted for presentation by NeurIPS AI for Earth Sciences Workshop 2020.

• 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.

• 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.

• 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.

• 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.

• 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%
  

• 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.

• 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.
  Best Student Poster Award (Honorable Mention) at Georgia Statistics Day 2019
  

• 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.

• 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.

• 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%
  

• 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.

• 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 Papers

• 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.

• Data-driven optimization for police beat design in South Fulton, Georgia. (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

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

Thesis

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