Zhiqiang Tao
Assistant Professor
School of Information
Golisano College of Computing and Information Sciences
Zhiqiang Tao
Assistant Professor
School of Information
Golisano College of Computing and Information Sciences
Select Scholarship
Published Conference Proceedings
Bai, Yue, et al. "Parameter-Efficient Masking Networks." Proceedings of the Advances in Neural Information Processing Systems 35 (NeurIPS 2022), Nov 2022, New Orleans. Ed. Alice H. Oh, et al. New Orleans, LA: Curran Associates, Inc., Print.
Yang, Xueying, et al. "Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty." Proceedings of the Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Oct, 2022, Atlanta, GA, USA. Ed. Mohammad Al Hasan and Li Xiong. Atlanta, GA, USA: Association for Computing Machinery, Print.
Journal Paper
Wang, Qianqian, et al. "Multi-View Subspace Clustering via Structured Multi-Pathway Network." IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. (2022): 1-7. Print.
Currently Teaching
ISTE-612
Information Retrieval and Text Mining
3 Credits
This is the second course in a two-course sequence that provides students with exposure to foundational information sciences and technologies. Topics include internet middleware technologies, data and text analytics, and information visualization. Note: One year of programming in an object-oriented language, a database theory course, a course in Web development, and a statistics course is needed.
ISTE-780
Data Driven Knowledge Discovery
3 Credits
Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains.