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. Accepted by Scandinavian Journal of Statistics.
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.