Chun-Nam Yu

Education

Cornell University, August 2004 - August 2010 
Ph.D. in Computer Science (with minor in Applied Mathematics)

Wadham College, Oxford University, UK, October 2001 - July 2004
B.A. in Mathematics & Computer Science (First Class Honours)

Biography

Chun-Nam Yu joined Bell Labs as a Member of Technical Staff in 2012. He received his BA degree in Mathematics and Computer Science from Oxford University in 2004, and PhD degree in Computer Science in 2010 from Cornell University, under the supervision of Thorsten Joachims. He was a postdoctoral fellow at the Alberta Innovates Centre of Machine Learning (AICML) at the University of Alberta from 2010 to 2012, working with Russ Greiner on biomedical machine learning problems. His research interests include structured output prediction, support vector machines, kernel methods, and on the large-scale training of these models. At Bell Labs he studied time series modeling problems arising from network traffic and smart grid data, and also the online optimization of network diagnostic processes.

Professional Activities

Program Committee Member: 
International Conference on Machine Learning (ICML) 2011, 2012, 2013, 2014
Neural Information Processing Systems (NIPS) 2009, 2010, 2012, 2013, 2014
Uncertainty in Artificial Intelligence (UAI) 2011
European Conference on Machine Learning (ECML) 2008, 2010, 2013, 2014

Journal Article Reviewer: 
Journal of Machine Learning Research (JMLR)
Journal of Artificial Intelligence Research (JAIR)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
IEEE Transactions on Neural Networks (TNN)
Neurocomputing

Grant Proposal Reviewer: 
NSERC Discovery Grants, Canada, 2012


Selected Articles and Publications

J. Wen, C.-N. Yu, R. Greiner, Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification, International Conference on Machine Learning (ICML), 2014

P. Mirowski, S. Chen, T. Ho, C.-N. Yu, Demand Forecasting in Smart Grids, Bell Labs Technical Journal, Volume 18, Number 4, March 2014

S. Ravanbakhsh, C.-N. Yu, R. Greiner, A Generalized Loop Correction Method for Approximate Inference in Graphical Models, International Conference on Machine Learning (ICML), 2012

C.-N. Yu, Transductive Learning of Structural SVMs via Prior Knowledge Constraints, International Conference on Artificial Intelligence and Statistics (AISTATS), 2012

C.-N. Yu, R. Greiner, H.-C. Lin, V. Baracos, Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors, Advances in Neural Information Processing Systems (NIPS), 2011 

T. Joachims, T. Hofmann, Y. Yue, C.-N. Yu, Predicting Structured Objects with Support Vector Machines, Communications of the ACM, Research Highlight, 52(11):97-104, November 2009

C.-N. Yu, T. Joachims, Learning Structural SVMs with Latent Variables, International Conference on Machine Learning (ICML), 2009

T. Joachims, T. Finley, C.-N. Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, 2009, volume 77(1)

T. Joachims, C.-N. Yu, Sparse Kernel SVMs via Cutting-Plane Training, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2009

C.-N. Yu, T. Joachims, Training Structural SVMs with Kernels Using Sampled Cuts, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2008

C.-N. Yu, T. Joachims, R. Elber, J. Pillardy, Support Vector Training of Protein Alignment Models, Proceeding of the International Conference on Research in Computational Molecular Biology (RECOMB), 2007