About Me

My Photo

Contact Information

5138 Upson Hall
Dept. of Computer Science
Cornell University
Email: cnyu AT cs.cornell.edu
Office: (607) 255-9201

I have moved to here.

Starting from September 2010 I am a postdoctoral fellow at AICML at the Department of Computing Science at the University of Alberta, working with Russ Greiner. Prior to coming to Edmonton I obtained my PhD in Computer Science at Cornell University, under the supervision of Thorsten Joachims. Before that I studied at Wadham College at Oxford for my undergraduate degree, and I grew up in Hong Kong.

I am on the job market this year, applying for both academic and industrial positions. Here are my CV, research statement, and teaching statement.

Research Interests.

My main research interest is in structured output prediction, especially methods based on large-margin principles. I also work on support vector machines, kernel methods, and on the large-scale training of these models. Here at AICML I also started looking at machine learning problems arising from biomedical data.


    • Transductive Learning of Structural SVMs via Prior Knowledge Constraints (pdf, with supplementary materials)
    • C.-N. Yu
    • International Conference on Artificial Intelligence and Statistics (AISTATS), 2012
    • Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors (pdf) (code)
    • C.-N. Yu, R. Greiner, H.-C. Lin and V. Baracos
    • Neural Information Processing Systems (NIPS), 2011
    • Improved Learning of Structural Support Vector Machines: Training with Latent Variables and Nonlinear Kernels (pdf)
    • C.-N. Yu
    • PhD Thesis, Cornell University, 2010
    • Predicting Structured Objects with Support Vector Machines
    • T. Joachims, T. Hofmann, Y. Yue and C.-N. Yu
    • Communications of the ACM, Research Highlight, 52(11):97-104, November 2009
    • Sparse Kernel SVMs via Cutting-Plane Training (pdf)
    • T. Joachims and C.-N. Yu
    • ECML PKDD 2009 special issue, Machine Learning Journal
    • (Best Machine Learning Paper Award)
    • Learning Structural SVMs with Latent Variables (pdf) (code)
    • C.-N. Yu and T. Joachims
    • International Conference on Machine Learning (ICML), 2009
    • (An earlier version appeared in NIPS 2008 Structured Input-Structured Output Workshop (pdf) )
    • Cutting-Plane Training of Structural SVMs (pdf)
    • T. Joachims, T. Finley and C.-N. Yu
    • Machine Learning Journal, 77(1):27-59
    • Training Structural SVMs with Kernels Using Sampled Cuts (pdf) (software)
    • C.-N. Yu and T. Joachims
    • Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2008
    • Support Vector Training of Protein Alignment Models (pdf) (software)
    • C.-N. Yu, T. Joachims, R. Elber, J. Pillardy
    • Proceedings of the International Conference in Research in Computational Biology (RECOMB), 2007
    • Training Protein Threading Models using Structural SVMs (pdf)
    • C.-N. Yu, T. Joachims, R. Elber
    • ICML Workshop on Learning in Structured Output Spaces, 2006