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Dr. Xing Fang

Associate Professor of Computer Science
School Information Technology
  • About
  • Education
  • Awards & Honors
  • Research

Current Courses

441.001Big Data

494.001Graduate Directed Project

340.001Introduction To Artificial Intelligence

179.004Introduction To Data Structures

Teaching Interests & Areas

Python, C/C++, Java

Research Interests & Areas

deep learning, natural language processing, machine learning

Ph D Computer Science

North Carolina A&T State University
Greensboro NC USA

MS Computer Science

North Carolina A&T State University
Greensboro NC USA

MS Information Assurance

Dakota State University
Madison, SD, USA

BS Electrical Engineering

Northwestern Polytechnical University
Xi'an, Shaanxi, China

Impact Award

University College

Impact Award

University College

Conference Proceeding

Tao, J., Fang, X., & Zhou, L. Unsupervised Deep Learning for Fake Content Detection in Social Media. HICSS (2021)
X. Fang and J. Tao, "A Transfer Learning based Approach for Aspect Based Sentiment Analysis," 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, 2019, pp. 478-483.
Nadeem, Mutahir, Ochaun Marshall, Sarbjit Singh, Xing Fang, and Xiaohong Yuan. "Semi-supervised deep neural network for network intrusion detection." (2016).

Journal Article

Chen, Y., Zhu, J., Wan, L., Fang, X., Tong, F., & Xu, X. Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments. Applied Acoustics/ Elsevier 186 (2022)
Chen, Y., Tang, Y., Fang, X., Wan, L., Tao, Y., & Xu, X. PB-ACR: Node Payload Balanced Ant Colony Optimal Cooperative Routing for Multi-Hop Underwater Acoustic Sensor Networks. IEEE Access (2021)
Chen, Y., Zheng, K., Fang, X., Wan, L., & Xu, X. QMCR: A Q-Learning-Based Multi-Hop Cooperative Routing Protocol for Underwater Acoustic Sensor Networks. IEEE China Communications (2021)
Fang, X. Making recommendations using transfer learning. Neural Comput & Applic (2021)
Yang, F., Pluth, T., Xing, X., Francq, K., Jurjovec, M., & Tang, Y. Advanced machine learning application for odor and corrosion control at a water resource recovery facility. Water Environment Research, Wiley Journal (2021)


Deep learning and AI. Physics Informal Seminar Series. (2019)
Develop Advanced Models to Predict and Optimize Chemical Dosage for Odor and Corrosion Control. the WEFTEC 2019 Intelligent Water Systems Challenge. (2019)
Introduction to Deep Learning. MAT 443. Illinois State Unviersity. (2016)

Grants & Contracts

New Faculty Start-up Program. Illinois State University. (2016)