Zhiqiang Tao
Assistant Professor
School of Information
Golisano College of Computing and Information Sciences
Office Location
Zhiqiang Tao
Assistant Professor
School of Information
Golisano College of Computing and Information Sciences
Education
BE, Tianjin University (China); MS, Tianjin University (China); Ph.D., Northeastern University
Areas of Expertise
Machine Learning
Computer Vision
Computational Imaging
Data Science
Select Scholarship
Published Conference Proceedings
Wang, Jiamian, et al. "Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Ed. N/A. Seattle, WA: n.p., Web.
Wang, Jiamian, et al. "Diffusion-Inspired Truncated Sampler for Text-Video Retrieval." Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). Ed. n/a. Vancouver, Canada: n.p., 2024. Web.
Wang, Jiamian, et al. "Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging." Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). Ed. n/a. Vancouver, Canada: n.p., 2024. Web.
Sun, Guohao, et al. "SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant." Proceedings of the European Conference on Computer Vision (ECCV). Ed. n/a. Milano, Italy: n.p., 2024. Web.
Sun, Guohao, et al. "Self-Training Large Language and Vision Assistant for Medical Question-Answering." Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP). Ed. n/a. Miami, FL: n.p., 2024. Web.
Wang, Jiamian, et al. "Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution." Proceedings of the International Conference on Computer Vision (ICCV). Ed. N/A. Paris, French: n.p., 2023. Print.
Wang, Yuan, et al. "An Empirical Study of Selection Bias in Pinterest Ads Retrieval." Proceedings of the Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023. Ed. Ambuj Singh, et al. Long Beach, CA: n.p., 2023. Web.
Sapkota, Hitesh, et al. "Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training." Proceedings of the Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Ed. N/A. New Orleans, LA: n.p., Print.
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, Yuan, Zhiqiang Tao, and Yi Fang. "A Unified Meta-learning Framework for Fair Ranking with Curriculum Learning." IEEE Transactions on Knowledge and Data Engineering. (2024): PrePrints pp. 1-12,. Web.
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
IDAI-780
Capstone Project
3 Credits
Graduate capstone project by the candidate on an appropriate topic as arranged
between the candidate and the research advisor.
ISTE-612
Information Retrieval and Text Mining
3 Credits
This course provides students with exposure to foundational data analytics technologies, focusing on unstructured data. Topics include unstructured data modeling, indexing, retrieval, text classification, text clustering, and information visualization.
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.