Yao Xie

Point processes

Point processes with event time uncertainty.
Xiuyuan Cheng, Tingnan Gong, Yao Xie.

Spatio-temporal-network point processes for modeling crime events with landmarks.
Zheng Dong, Jorge Mateu, Yao Xie.

Atlanta gun violence modeling via nonstationary spatio-temporal point processes.
Zheng Dong, Yao Xie.

Deep graph kernel point processes.
Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie.
Spotlight Paper at Temporal Graph Learning Workshop @ NeurIPS 2023 (Long Paper).

Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data.
Zheng Dong, Shixiang Zhu, Yao Xie, Jorge Mateu, Francisco J. Rodriguez-Cortes.
Journal of the Royal Statistical Society: Series C (Applied Statistics). May 2023. Vol. 72, No. 2, pp. 368–386.
Coverage by health.it.analytics and CMU.

Crime linkage detection by spatial-temporal-textual point processes.
Shixiang Zhu, Yao Xie.
Annals of Applied Statistics. Vol. 16, No. 2, pp. 1151-1170. 2022.
Selected to be presented in "The Best of AOAS" Session at JSM 2022, JSM 2023.

Sequential change-point detection for mutually exciting point processes over networks.
Haoyun Wang, Liyan Xie, Yao Xie, Alex Cuozzo, Simon Mak.
Technometrics, Vol. 65, No. 1, pp. 44-56, 2023.

Online score statistics for detecting clustered change in network point processes.
Rui Zhang, Haoyun Wang, Yao Xie.
Sequential Analysis. 42:1, pp. 70-89. 2023.

Online detection of supply chain network disruptions using sequential change-point detection for Hawkes processes.
Khurram Yamin, Haoyun Wang, Montreuil Benoit, and Yao Xie.
9th International Physical Internet Conference (IPIC).

Spatio-temporal point processes with deep non-stationary kernels.
Zheng Dong, Xiuyuan Cheng, Yao Xie.
ICLR 2023.

Tensor kernel recovery for spatio-temporal Hawkes processes.
Heejune Sheen, Xiaonan Zhu, Yao Xie.
IEEE Transactions on Signal Processing. Vol. 70, pp. 5859-5870. 2022.

Crime linkage detection by spatial-temporal-textual point processes.
Shixiang Zhu, and Yao Xie.
Annals of Applied Statistics. Vol. 16, No. 2, pp. 1151-1170. 2022.

Spatio-temporal point processes with attention for traffic congestion event modeling.
Shixiang Zhu, Ruyi Ding, Pascal Van Hentenryck, and Yao Xie.
IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 7, pp. 7298 - 7309, July 2022.

Neural spectral marked point processes. (poster)
Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie.
ICLR 2022.

Deep attention point processes with neural spectrum Fourier kernel.
Shixiang Zhu, Minghe Zhang, Ruyi Ding, and Yao Xie.
AISTATS 2021. (Oral)

Goodness-of-fit test for mismatched self-exciting processes.
Song Wei, Shixiang Zhu, Minghe Zhang, and Yao Xie.
AISTATS 2021.

Imitation learning of spatio-temporal point processes.
Shixiang Zhu, Shuang Li, Zhigang Peng, and Yao Xie.
IEEE Transactions on Knowledge and Data Engineering. Vol. 34, No. 11, pp. 5391 - 5402 2021.
(Preliminary results presented at NeurIPS AI for Earth Sciences Workshop, 2020.)

Causal graph recovery for sepsis-associated derangements via interpretable Hawkes networks
Song Wei, Yao Xie, Christopher S. Josef, and Rishikesan Kamaleswaran.
ICML 2021 Workshop on Interpretable Machine Learning in Healthcare (IMLH).

Uncertainty quantification for inferring Hawkes networks.
Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, and Yao Xie.
NeurIPS 2020.

Sequential adversarial anomaly detection for one-class event data.
Shixiang Zhu, Henry Yuchi Shaowu, Minghe Zhang, Yao Xie.
INFORMS Journal on Data Sciences. Nov. 2023. No. 2, Vol. 1. pp. 45-59.
Conference version appeared in ICASSP 2020.

Temporal logic point processes.
S. Li, L. Wang, R. Zhang, X. Chang, X. Liu, Y. Xie, Y. Qi, L. Song.
ICML 2020.

Spatio-temporal models

Early detection of COVID-19 hotspots using spatio-temporal data.
S. Zhu, A. Bukharin, L. Xie, K. Yamin, S. Yang, P. Keskinocak, and Y. Xie.
Minor Revision, IEEE Journal Selected Topics in Signal Processing (JSTSP)
Special Issue on Signal Analysis for Detection and Monitoring of Contagious Diseases.
Conference version presented by ICML 2021 Time Series Workshop (Best Paper Award, Second Place.)

Deep graph kernel point processes.
Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie.
Spotlight Paper at Temporal Graph Learning Workshop @ NeurIPS 2023 (Long Paper).

Quantifying grid resilience against extreme weather using large-scale customer power outage data.
Shixiang Zhu, Rui Yao, Yao Xie, Feng Qiu, Yueming (Lucy) Qiu, and Xuan Wu. Minor revion, INFORMS Journal on Data Science.

Multi-resolution spatio-temporal prediction with application to wind power generation.
Zheng Dong, Shixiang Zhu, Hanyu Zhang, Yao Xie, Pascal Van Hentenryck.

Tides need STEMMED: A locally operating spatio-temporal mutually exciting point process with dynamic network for improving opioid overdose death prediction.
Che-Yi Liao, Gian-Gabriel Garcia, Kamran Paynabar, Zheng Dong, Yao Xie, Mohammad S. Jalali.
Major Revision, Manufacturing and Service Operations Management (MSOM).
Preliminary version presented at 2023 INFORMS Workshop on Data Science.
Winner, 2022 SMDM Lee B. Lusted Prize in Quantitative Methods & Theoretical Developments.

Solar radiation anomaly events modeling using spatio-temporal mutually interactive processes.
Minghe Zhang, Chen Xu, Andy Sun, Feng Qiu, Yao Xie.
In revision, INFORMS Journal on Data Science.
Conference version presented at INFORMS Conference on Service Science (ICSS 2021).

Spatio-temporal wildfire prediction using multi-modal data.
Chen Xu, Daniel A. Zuniga Vazquez, Rui Yao, Feng Qiu, Yao Xie.
IEEE Selected Areas in Information Theory (JSAIT). May 2023. Vol. 4, pp. 302-313.

Early detection of COVID-19 hotspots using spatio-temporal data.
Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, and Yao Xie.
IEEE Journal Selected Topics in Signal Processing (JSTSP). Vol. 16, No. 2, pp. 250-260, 2022.
Special Issue on Signal Analysis for Detection and Monitoring of Contagious Diseases.
Conference version presented by ICML 2021 Time Series Workshop (Best Paper Award, Second Place.)

High-resolution spatio-temporal model for county-level COVID-19 activity in the U.S..
Shixiang Zhu, Alexander Bukharin, Liyan Xie, Mauricio Santillana, Shihao Yang, Yao Xie.
ACM Transactions on Management Information Systems, Vol. 12, No. 4, pp. 1-20, December 2021.

Causal Graph and Social Network Analysis for COVID-19.
Shaoou Chen, Yao Xie, Shihao Yang.
Asilomar 2021.

: An efficient score statistic for spatial-temporal surveillance.
J. Chen, S.-H. Kim, and Y. Xie.
Sequential Analysis, 39:4, pp. 563-592, 2020.
(Finalist of INFORMS QSR Best Student Paper Contest, 2017.)

Adversarial anomaly detection for marked spatio-temporal streaming data.
S. Zhu, H. Shaowu, Y. Xie.
ICASSP 2020.

To reduce or not to reduce: A study on spatio-temporal surveillance.
J. Chen, C. Park, S.-H. Kim, and Y. Xie.
Environmental and Ecological Statistics (EEST). Vol. 26, No. 3, pp. 217-238 September 2019.

Learning temporal point processes via reinforcement learning.
S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song.
NeurIPS 2018 (Spotlight).