Big Data and Statistical Learning
Rimkus, M., Kokoszka, P. Duan, D., Wang, X. and Wang, H. (2025). Graph neural networks for the localization of faults in partially observed regional transmission systems. Scandinavian Journal of Statistics, 52, 572–594.
Muramudalige, S. R., Jayasumana, A.P. and Wang, H. (2023). A feature mapping technique for complex data object generation with likelihood and deep generative approaches. IEEE Access, 11, 136643-136653.
Shirazi, H., Shashika, R.M., Ray, I., Jayasumana, A.P. and Wang, H. (2023). Adversarial autoencoder data synthesis for enhancing machine learning-based phishing detection algorithms. IEEE Transactions on Services Computing, 16, 2411-2422.
Fang, L., Cheng, X., Wang, H. and Yang, L. (2019). Idle Time Window Prediction in Cellular Networks with Deep Spatiotemporal Modeling. IEEE Journal on Selected Areas in Communications, 37, 1441-1454.
Fang, L., Cheng, X., Wang, H. and Yang, L. (2018). Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks. IEEE Internet of Things Journal, 5, 3091-3101.
Fang, L., Cheng, X., Yang, L. and Wang, H. (2018). Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation. Journal of Communications and Information Networks, 3, 31-38.
Sienkiewicz, E., Song, D., Breidt, F.J. and Wang, H. (2016). Sparse Functional Dynamical Models --- a Big Data Approach. Journal of Computational and Graphical Statistics, 26, 319-329.
R packages:
- Big Data: Maximum Likelihood Estimate and Regularized MLE for Generalized Linear Models bdglm.
- Big Data: Functional Dynamic Multiple-Input Single-Output Models for Neural Spikes bdmiso, installation instructions.