2007/09 - 2012/07 Ph.D in Computer Science
Computer Science and Technology, Tsinghua University, Beijing, China
Thesis: Discriminative Probabilistic Latent Space Learning
and Model Complexity
2003/09- 2007/07 B.S. on Computer Science
School of Computer Science, NorthWestern Polytechnical University, Xiaan, China
2012/09 - 2014/07 Postdoc Researcher
Department of Computer Science and Technology, Tsinghua University, China
2010/01 - 2011/02 Visiting Scholar
Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
2008/09 - 2008/10 Visiting Student
Department of Informatics, University of Hamburg, Hamburg, Germany
My main research interests lie in both machine learning and computational biology. For machine learning, I am interested in developing Predictive Latent Variable Models for learning discriminative and interpretable latent representations using large margin learning in the formalism of probabilistic graphical models based on multi-view data and relational network data; For computational biology, I am interested in developing statistical machine learning techniques for large-scale metagenomic data analysis as well as discovering the underlying biological interpretations of metagenomics.
1. Ning Chen, J. Zhu, F. Xia and B. Zhang. Discriminative Relational Topic Models, IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2015. (in press)
2. Ning Chen, F. Sun, B. Zhang. Learning Harmonium Models with Infinite Latent Features, IEEE Transaction on Neural Networks and Learning Systems (TNNLS), Vol. 25(3),520–532, 2014.
3. Ning Chen, J. Zhu, F. Sun, E.P. Xing. Large Margin Predictive Latent Subspace Learning for Multi-view Data Analysis, IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 34(12), 2365–2378, 2012.
4. Ning Chen, J. Zhu, J. Chen and B. Zhang. Dropout Training for Support Vector Machines, AAAI conference on Arti_cial Intelligence (AAAI), 2014.
5. Ning Chen, J. Zhu, F. Xia and B. Zhang. Generalized Relational Topic Models with Data Augmentation, in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013.
6. Ning Chen, J. Zhu, E.P. Xing. Predictive Subspace Learning for Multi-view Data: A Large Margin Approach, in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, 2010.
7. F. Xia, Ning Chen, et. al. Max Margin Latent Feature Relational Models for Entity-Attribute Networks. in International Joint Conference on Neural Networks (IJCNN), Beijing, 2014.