Publication

1. Deep Graph Kernel Point Processes.

Z. Dong, M. Repasky, X. Cheng, and Y. Xie.

• Oral spotlight paper, Temporal Graph Learning workshop @ Neurips 2023.

2. Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes.

Z. Dong, Z. Fan, and S. Zhu.

• Accepted, SyntheticData4ML workshop @ Neurips 2023.

3. Multi-resolution Spatio-temporal Prediction with Application to Wind Power Generation.

Z. Dong, H. Zhang, S. Zhu, Y. Xie, and P. Van Hentenryck.

• Finalist, 2023 INFORMS QSR Best Student Paper Competition.

• Finalist, Best Applied Paper for 2023 INFORMS Workshop on Data Mining and Decision Analytics.

• Best Student Paper Nominee & full presentation at 2022 INFORMS Workshop on Data Science.

1. Non-stationary Spatio-temporal Point Process Modeling for High-resolution COVID-19 Data. [arxiv][Github][Poster]

Z. Dong, S. Zhu, Y. Xie, J. Mateu, and F. J. Rodriguez-Cortes.

Journal of the Royal Statistical Society, Series C (JRSSC), Volume 72, Issue 2, May 2023, Pages 368–386.

• Winner, Best Applied Paper for 2022 INFORMS Workshop on Data Mining and Decision Analytics.

• Runner-up, 2022 INFORMS Poster Competition.

• Accepted for full presentation at 2022 INFORMS Workshop on Data Science.

1. Spatio-temporal Point Processes with Deep Non-stationary Kernels. [arxiv]

Z. Dong, X. Cheng, and Y. Xie.

International Conference on Learning Representations (ICLR), 2023.

• Student Presentation Award, 2022 Georgia Statistics Day.

2. Neural Spectral Marked Point Processes. [arxiv] [Github]

S. Zhu, H. Wang, Z. Dong, X. Cheng, and Y. Xie. 

International Conference on Learning Representations (ICLR), 2022.

3. Sequential Vessel Trajectory Identification Using Truncated Viterbi Algorithm.

Z. Dong, Y. Yang, and Y. Xie.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.