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Promises of Data from Emerging Technologies for Trans-portation Applications: Puget Sound Region Case Study. X. Ban, C. Chen, F. Wang, J. Wang and Y. Zhang. Federal Highway Administration, 2018. 

Understanding GPS and Mobile Phone Data for Origin-Destination Analysis. C. Chen, X. Ban, F. Wang, J. Wang, C. Siddique, R. Fan and J. Lee. Federal Highway Administration, 2017. 

Data Poisoning Attacks and Infrastructure-Enabled Solutions for Traffic State Estimation and Prediction, 2019 – present

• Assisting NSF proposal writing; Preliminary analysis of vulnerability of common TSEP models;

• Developing an infrastructure-enabled solution to detection and correcting data poisoning attacks on vehicular data.

Scenario Modeling for Post-Covid Return to Work and Transit Usage, Challenge Seattle Association, 2020 – current.

• Data collection and processing; scenario building; modeling regional traffic and transit usage;

• Evaluating quality of big mobility data (e.g., Google data and Apple data);

• Analyzing and communicating findings with local agencies (e.g., City of Seattle, PSRC, WSDOT, King County

• Metro).

Promises of Data from Emerging Technologies for Transportation Applications, Federal Highway Administration (FHWA), 2018-Current. Details

• Mining real-world, noisy big datasets to address practical challenges; understanding properties of big data by comparing them against conventional sensor data; leveraging machine learning to predict travel patterns changes.

3-population 3-scale social network model to assess disease dispersion, National Institute of Health, 2015-Current. Details

• Data-driven modeling of human travel patterns in an entire region and evaluating their role in influenza spreading.

Real-time prediction of movement patterns during disaster times, Facebook research, 2018-2019. Details

• Leveraging a 3TB dataset to assess impacts of Hurricane Harvey on human trajectories in 100+ counties; inferring evacuation patterns of each person; extracting and assembling features from noisy data; learning and predicting evacuation behaviors via both machine learning and interpretable conventional models.

The connected traveler: a framework to reduce energy use in transportation, Department of Energy, 2015-2018. Details

• Developed a statistical model for learning travel behaviors and promoting personalized recommendations in real-time; developed an online experimental system for testing. 

Understanding GPS and Mobile Phone Data for Origin-Destination Analysis, FHWA, 2017. Details

• Comparing and understanding heterogeneous big data for estimating Origin-Destination travel demand.

Data modeling and analysis

• Stochastic Optimization; Stochastic Modeling of Scientific Data; Computational Methods for Data Analysis. 

Statistical learning

• Mathematical Statistics; Statistical Methods for Spatial Data; Statistical Learning. 

Machine learning and cloud computing

• Machine Learning for Big Data; Artificial Intelligence; Reinforcement Learning; Cloud Computing.

I play badminton, love running, and receive ideas walking in the nature.