Jiye Liang
 |
Jiye Liang |
Biography
I am a ''Shuang Long Scholar'' Distinguished Professor at the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, China. I received my Ph.D. degree from the Department of Computer Science and Information Technology at La Trobe University, Australia. After that, I completed two Postdoctoral Fellowship positions with the Department of Mathematics and Statistics, La Trobe University, Australia, and the Department of Information Technology in Education, South China Normal University, China, respectively. I have published in top-tier journals and conferences, including IEEE TPAMI, Artificial Intelligence, IEEE TKDE, IEEE TNNLS, IEEE TAI, IEEE TCYB, IEEE TII, IEEE TITS, Computers & Education, BJET, NeurIPS, ICML, IJCAI. I am a member of IEEE, a member of the China Computer Federation (CCF), a member of the Chinese Association for Artificial Intelligence (CAAI), and an accredited member of the Australian Mathematical Society (AustMS). I am a regular reviewer for top journals, including IEEE TPAMI, IEEE TNNLS, IEEE TAI, IEEE TCYB, IEEE TKDE, IEEE TITS, IEEE TII, IEEE TETCI, Pattern Recognition, Neural Networks, Information Sciences, Neurocomputing, etc..
As the leading guest editor, I organized a special issue on “Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications” in IEEE TNNLS, and a special session on "Recent Advances in Deep Learning for Graphs" in the 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science (LOD2022). I am a PC member at top conferences, including NeurIPS, ICML, ICLR, AAAI, IJCAI, KDD, WSDM, ICME, AJCAI, etc, a member of IEEE Task Force on Learning for Structured Data. I am an Associate Editor of Neural Networks, Associate Editor of Applied Intelligence, an Associate Editor of Soft Computing, an Associate Editor of Neural Processing Letters.
I am looking for highly-motivated students to work with me on the exciting area of graph neural networks, graph representation learning, geometric deep learning, etc.