Yaqing Wang
contrastive learning
multimodal learning
fairness & equity
ml: bias and fairness
ml: multi-class/multi-label learning & extreme classification peai: bias
cross-source medical pre-training
3
presentations
2
number of views
SHORT BIO
Yaqing is a final year Ph.D. Student under the supervision of Dr. Jing Gao in School of Electrical and Computer Engineering, Purdue University. His research interests lie in the intersection of data mining, machine learning, and NLP areas. In particular, he is interested in developing data-efficient and parameter-efficient cutting-edge models for real-world application. He has published papers in several top-tier data science conferences, such as KDD, WWW, AAAI, EMNLP, ICDM, CIKM and SDM as well as journals like Bioinformatics. More details can be found in https://yaqingwang.github.io/ .
Presentations
Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources
Xiaochen Wang and 8 other authors
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu and 5 other authors
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
Yaqing Wang and 3 other authors