Yuyang Gao’s personal site

About Me

Welcome! I am a Ph.D. student in the Department of Computer Science at Emory University. My Ph.D. advisor is Dr. Liang Zhao. I earned my B.S. degree in Computer Science from Shandong University, China in 2014, and M.S. in Computer Science from George Mason University in 2018 with the Distinguished Academic Achievement. My research focuses on data mining and machine learning techniques that can handle complex structured data, such as spatiotemporal and graph-structured data. In addition, I am also interested in opening the ‘black-box’ of the deep learning models via designing the bio-inspired model architectures as well as via enhancing their interpretability and explainability through new technique called explanation supervision.

I have published many peer-reviewed full research papers in top-tier conferences and journals such as KDD, AAAI, ICDM, TKDD, TKDE, and Neural Networks. I have also served as the Independent Reviewer and PC member for many top-tier conferences and journals such as KDD, AAAI, CSCW, TKDD, JMLR, TNNLS, and TOIS. You can find more about my works in my Publication, and more about me in my CV.

I am at the final year of my PhD program and am on the job market this year. Please feel free to contact me via email (yuyang.gao@emory.edu)

News

  • 05/2022: Our new work on robust visual explanation supervision has been accepted by KDD 2022! Paper, code, as well as our human-labeled explanation data are now available at Github here!
  • 03/2022: Serve as the PC member for KDD 2022.
  • 03/2022: Serve as the Independent Reviewer for journals TOIS, TKDD, JMLR.
  • 02/2022: Serve as the PC member for CSCW 2022.
  • 09/2021: Serve as the PC member for AAAI 2022.
  • 08/2021: Our paper on learning to explain GNNs has been accepted by ICDM 2021! Paper, code, as well as our human-labeled explanation data are now available at Github here!
  • 08/2021: Our paper on neuro-inspired deep learning has been accepted by Neural Networks! Paper and code will be available soon!
  • 06/2021: Our paper on modeling patient health stage development with dynamic attributed graphs has been accepted by TKDE!