About Me
I am Qiang (John) Yang, an incoming postdoctoral researcher at Health Outcomes & Biomedical Informatics in University of Florida in Prof. Rui Yin’s group. I major in the graph neural networks and the interpretable learning as well as their applications on the heterogeneous graphs. I also focus on the large language models to solve the reseach problems on biomedical science. I received my Ph.D. degress in King Abdullah University of Science and Technology (KAUST) in 2023, under the supervision of Prof. Xiangliang Zhang and Prof. Xin Gao at the lab of Machine Intelligence and kNowledge Engineering (MINE) and Structural and Functional Bioinformatics Group (SFB). My dissertation focuses on interpretable learning on heterogeneous graphs, which reveals the predicted results by heterogeneous graph models by generating understandable graphs.
I obtained Master degree with outstanding Master student award from Soochow University advised by Prof. Zhixu Li. During this processing, I mainly focus on database systems. Particularly, the overall goal is to improve data quality where I did some researches like record matching and data cleaning. Also, the exploration on the knowledge graphs (KGs) was done, like KG construction, representation, and so on.
Academic Background
- November 2023 - Now: University of Florida (Postdoctoral Researcher)
- January 2019 - November 2023: King Abdullah University of Science and Technology (Ph.D Degree)
- December 2022 - March 2023: Fudan University (Visiting Student)
- September 2014 - June 2017: Soochow University (Master Degree)
- May 2016 - November 2016: King Abdullah University of Science and Technology (Visiting Student)
- September 2010 - July 2014: Pingdingshan University (Bachelor Degree)
Research Interests
- Machine Learning
- Data Mining
- Graph Representation Learning
- Interpretable Leaning
- Large Language Model
- Multimodal Learning
- Sentiment Analysis
- Sound Analysis
Selected Publications (Read More)
- Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, and Xiangliang Zhang. 2023. Greedy Policy-based Perturbation for Counterfactual Learning on Heterogeneous Graphs. in KDD.
- Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V Chawla, and Xiangliang Zhang. 2023. Cross-domain Few-shot Graph Classification with a Reinforced Task Coordinator. In AAAI.
- Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, and Xiangliang Zhang. 2023. Interpretable Research Interest Shift with Temporal Heterogeneous Graphs Detection. in WSDM.
- Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng, Qiang Yang, Qishen Zhang, Xin Gao, and Xiangliang Zhang. 2023. A Topic-aware Summarization Framework with Different Modal Side Information. In SIGIR.
News
- Will be updated soon.