Xun Zhou

Xun Zhou, Ph.D.

Associate Professor, Henry B. Tippie Research Fellow
Department of Business Analytics
Tippie College of Business
The University of Iowa

Office: S280 PBB
21 East Market Street, Iowa City, IA 52242-1994
xun-zhou@uiowa.edu | 319-384-3335

Dr. Xun Zhou is an Associate Professor in the Department of Business Analytics, Tippie College of Business at The University of Iowa. He is also affiliated with the Interdisciplinary Graduate Program in Informatics (IGPI) and the Applied Mathematical and Computational Sciences (AMCS) program. 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, Harbin, China in 2009 and 2007, respectively.

Opening: I'm looking for self-motivated students to work on spatial data mining and machine learning topics. Interested students should contact me with CV and transcript. Strong programming skills and Math/Stats/CS/EE background preferred.

Recent News

Research Interests

Selected Publications (Full List) (Google Scholar)

[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
[ICDM'20]
TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-term Decision Analysis
[ICDM'20]
cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction
[SIGSPATIAL'20]
COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks
[SIGSPATIAL'20]
Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data
[SIGSPATIAL'20]
Is Reinforcement Learning the Choice of Human Learners? A Case Study of Taxi Drivers
[WWW'20]
Inferring Passengers' Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning
[ICDM'19]
Unveiling Taxi Drivers' Strategies via cGAIL - Conditional Generative Adversarial Imitation Learning
[ICDM'19]
Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks
[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
[NeurIPS'18]
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
[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
[IEEE TKDE]
Focal-Test-Based Spatial Decision Tree Learning
[ACM TIST]
A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results
[ACM TIST]
Discovering Interesting Sub-paths with Statistical Significance from Spatio-Temporal Datasets
[ACM TIST]
DHPA: Dynamic Human Preference Analytics Framework - A Case Study on Taxi Drivers' Learning Curve Analysis
[IEEE TBD]
Optimizing Taxi Driver Profit Efficiency: A Spatial Network-based Markov Decision Process Approach
[IEEE TBD]
Sampling Big Trajectory Data for Traversal Trajectory Aggregate Query

Acknowledgement

My research has been supported by NSF, NVIDIA Corporation, and the Safer-Sim University Transportation Center.