Xun Zhou

Xun Zhou, Ph.D.


Bio: Dr. Xun Zhou was previously a tenured Associate Professor and Henry B. Tippie Research Fellow in the Department of Business Analytics, Tippie College of Business at The University of Iowa. He served as the director of graduate studies (DGS) during the 2022-2023 academic year. He was also an affiliated faculty member with the Interdisciplinary Graduate Program in Informatics (IGPI) and the Applied Mathematical and Computational Sciences (AMCS) programs. Since July 2023, he has been a Professor of Computer Science at Harbin Institute of Technology, Shenzhen (HIT-SZ) in China.
Xun received a Ph.D. in Computer Science from the University of Minnesota, Twin Cities in 2014, advised by Prof. Shashi Shekhar. Prior to that, Xun received his M.S. and B.E. degrees both in Computer Science and Technology from Harbin Institute of Technology in 2009 and 2007, respectively.

Contact: zhouxun2023 at hit.edu.cn | xun-zhou at uiowa.edu (phasing out)

Recent News

Research Interests

Selected Publications (Full List) (Google Scholar)

[EGIS]
Encyclopedia of GIS, 2nd Edition. 2017 (co-editor-in-chief).
Comprehensive reference book with over 200 peer-reviewed articles from 60 subfields. Over 220k downloads on Springer.com.
[NeurIPS'23]
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
[KDD'23]
ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM
[NeurIPS'22]
Explaining a Reinforcement Learning Agent via Prototyping
[ICDM'22]
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
(Best Paper Candidate)
[ICDM'21]
A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity
(Best Paper Award)
[KDD'20]
ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification
[KDD'20]
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis
[KDD'20]
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks
[SIGSPATIAL'20]
COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks
[WWW'20]
Inferring Passengers' Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning
[AAAI'19]
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
[SDM'19]
Dissecting the Learning Curve of Taxi Drivers: A Data-Driven Approach (Best Applied Data Science Paper Award)
[KDD'18]
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-temporal Data
[IJGIS]
Detecting Spatiotemporal Propagation Patterns of Traffic Congestion from Fine-grained Vehicle Trajectory Data
[IEEE TKDE]
Forecasting Gathering Events through Trajectory Destination Prediction: a Dynamic Hybrid Model
[IEEE TKDE]
Mining Spatio-temporal Reachable Regions with Multiple Sources from Massive Trajectory Data

Acknowledgement