Ke Yiping

Dr. Yiping Ke is an Assistant Professor with the School of Computer Science and Engineering, Nanyang Technological University. She obtained her Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology in 2008 and B.Sc. degree in Computer Science from Fudan University in 2003. Before joining NTU, she was a scientist with the Institute of High Performance Computing, A*STAR and a Research Assistant Professor at the Chinese University of Hong Kong.

Her experience has spanned over both academia and industry. She has published over 50 research papers in prestigious journals/conferences, receiving over 2,000 citations. She has served as program committee members and journal reviewers for ICML, NIPS, IJCAI, AAAI, KDD, VLDB, ICDM, SDM, CIKM, ACM TKDD, IEEE TNNLS, IEEE TKDE, VLDBJ, Machine Learning Journal, etc. She has filed 5 US/UK patents with industrial partners. She has secured and managed 9 research grants and industrial funds, and successfully rolled out developed technologies to a variety of domains including business analytics, bioinformatics, fluid dynamics, etc.

Research Interests

• AI and machine learning: transfer learning, machine learning for physics modelling
• Big data analytics: focusing on big data variety
• Data mining: network/graph analytics, clustering, correlation mining, text mining, stream mining
• Data management: indexing and query processing on large-scale graph data
• Applications: business analytics, computational fluid dynamics, bioinformatics

Articles (Journal)

Pengfei Wei, Yiping Ke, Chi Keong Goh. (2018). Feature Analysis of Marginalized Stacked Denoising Autoencoder for Unsupervised Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems, .

Pengfei Wei, Ramon Sagarna, Yiping Ke, Chi Keong Goh, Ong Yew Soon. (2017). Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression. Thirty-fourth International Conference on Machine Learning (ICML 2017).

Zhiqiang Xu, Yiping Ke, and Xin Gao. (2017). A Fast Stochastic Riemannian Eigensolver. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, James Cheng. (2014). GBAGC: A General Bayesian Framework for Attributed Graph Clustering. ACM Transactions on Knowledge Discovery from Data, 9(1).

Y. Ke, J. Cheng, W. Ng. (2008). Efficient Correlation Search from Graph Databases. IEEE Transactions on Knowledge and Data Engineering, 20(12), 1601-1615.