Shixiang Zhu

Assistant Professor of Data Analytics

Shixiang (Woody) Zhu is an assistant professor of data analytics at Heinz College of Information Systems and Public Policy. He received his PhD in Machine Learning at H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology.

Woody's research lies in the broad area of machine learning, data science, and optimization, with a particular interest in developing models for spatio-temporal data and dynamic networks and decision making under uncertainty. Much of his work aims to develop new methodologies for Social Good and address high-impact problems in various applications, including police operation, power system, transportation, healthcare and epidemiology, etc. His research in police zone districting entered the finalists of 2021 INFORMS Wagner prize and won the second place of 2019 INFORMS Doing Good with Good OR.

Courses Taught

95-828 - Machine Learning for Problem Solving

Publications

Selected Publications
1. S. Zhu, H. Wang, and Y. Xie. Data-driven optimization for Atlanta police zone design. 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.
2. S. Zhu and Y. Xie. Spatio-temporal-textual point processes for crime linkage detection. 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 (Only 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 proposed algorithm was implemented by Atlanta Police Department in their AWARE system in 2018.
3. S. Zhu, A. Bukharin, L. Xie, K. Yamin, S. Yang, P. Keskinocak, and Y. Xie. Early detection of COVID-19 hotspots using spatio-temporal data. 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 Workshop on Data Mining and Decision Analytics.
  - Best Paper Award (Honorable Mention) at ICML Time Series Workshop 2021.
  - A short version is accepted for presentation and highlighted as contributed talk by ICML Time Series Workshop 2021.
  - Excellent Poster Award at Georgia Statistics Day 2021.
4. S. Zhu, R. Ding, M. Zhang, P. Van Hentenryck, and Y. Xie. Spatio-temporal point processes with attention for traffic congestion event modeling. IEEE Transactions on Intelligent Transportation Systems. Vol. 23, Issue 7, July 2022.
5. S. Zhu, L. Xie, M. Zhang, R. Gao, and Y. Xie. Distributionally robust weighted k-nearest neighbors. Neural Information Processing Systems (NeurIPS), 2022.
6. S. Zhu, H. Wang, Z. Dong, X. Cheng, and Y. Xie. Neural spectral marked point processes. International Conference on Learning Representations (ICLR), 2022.
Selected Awards
1. Winner, Data Mining Best Applied Paper, 2022 INFORMS Workshop on Data Mining and Decision Analytics.
2. Finalist of 2021 INFORMS Wagner Prize.
3. Best Paper Award (Honorable Mention), ICML Time Series Workshop 2021.
4. Runner-up, Data Mining Best Applied Paper, 2020 INFORMS Workshop on Data Mining and Decision Analytics.
5. Second Place, 2019 INFORMS Doing Good with Good OR Student Paper Competition.
6. 2018 Smart 50 Award, Smart Cities Connect Conference & Expo.

Additional Information

Education