About me

Hi, I’m Zekai(Zach) Chen (陈泽锴)! I’m currently a Ph.D. student in Computer Science at the George Washington University working with Prof. Susan Cheng. My general research interests include Machine Intelligence and Data Mining. I mainly focus on interesting problems such as multi-task machine learning, sequence modeling, efficient deep learning, graph learning in IoT and anomaly detection. My research experience includes multi-task time series forecasting, sequence modeling acceleration in attention, graph structure learning for anomaly detection, and general data mining. For my research domains, one may follow my google scholar if interested. Also, I’m open in the job market of 2021 Fall! Please find my CV here.

Selected Publication

[1] Chen, Z., Chen, Z., Zhang, X., Pei, J., Pless, R., and Cheng, X., DCAP: Deep Cross Attentional Product Network for User Response Prediction, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021 (major revision, top journal in data mining, h5-index:81, IF: 7.628)

[2] Chen, Z., Chen, D., Cheng, X., and Zhang, X., Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT, IEEE Internet of Things Journal (IoTJ), 2021 (Accepted, top journal in IoT, IF: 11.75, H-index: 47)

[3] Chen, Z., E, J., Zhang, X., Sheng, H., and Cheng, X., Multi-Task Time Series Forecasting With Shared Attention, International Conference on Data Mining Data Transfer Learning (ICDM), page: 917-925, 2020 (Top conference in Data Mining, h5-index: 48, IF: 2.32)

[4] Yue, Y., Xu, P., Liu, Z., Chen, Z., etc., MeCP2 deletion impaired layer 2/3-dominant dynamic reorganization of cortical circuit during motor skill learning, Europe PMC, 2019 (IF: 2.478, citation: 1)

[5] Chen, Z., Zhu, S., and Djavanshir, R., Predicting Brand Advertisement Consumption on Facebook by Model Comparison, Journal of Global Business Management (JGBM), Volume 13, No. 2, Page 10-19, October 2017 issue (IF: 0.781)


[1] Chen, Z., Yang, H., Xiong H., and Zhang, X., Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021 (under review, top conference in data mining, h5-index: 90, IF: 3.875)

[2] Zhang, X., Chen, Z. (equal contribution), Zhuang, F., Li, W., Li, Y., Xiong, H., and Cheng, X., Learning Sharing Schemes: Multi-Task Multi-Step Time Series Forecasting with Variational Auto encoders, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021 (under review, top conference in data mining, h5-index: 90, IF: 3.875)

[3] Chen, Z., Shi, M., and Zhang, X., ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework, IJCAI International Joint Conference on Artificial Intelligence, 2021 (final notification, top conference in AI, h5-index: 95, IF: 2.79)