Publication

Journal Papers

• E. P. Xing, R. Curtis, G. Schoenherr, S. Lee, J. Yin, K. Puniyani, W. Wu, and P. Kinnaird, GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap, PLoS One, 2014

• S. Lee, F. Hormozdiari, C. Alkan, and M. Brudno, MoDIL: Detecting Small Indels from Clone-end Sequencing with Mixtures of Distributions, Nature Methods, 2009

• S. Lee and S. Choi, Landmark MDS Ensemble, Pattern Recognition, 2008

Conference Papers

• E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A New Platform for Distributed Machine Learning on Big Data, To appear in the 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015)

• S. Lee, A. Lozano, P. Kambadur, and E. P. Xing, An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations, The 19th International Conference on Research in Computational Molecular Biology (RECOMB 2015)

• S. Lee, J. Kim, X. Zheng, Q. Ho, G. A. Gibson, and E. P. Xing, On Model Parallelization and Scheduling Strategies for Distributed Machine Learning, Advances Neural Information Processing Systems 27 (NIPS 2014)

• H. Cui, J. Cipar, Q. Ho, J. Kim, S. Lee, A. Kumar, J. Wei, W. Dai, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E. P. Xing, Exploiting Bounded Staleness to Speed up Big Data Analytics, in USENIX Annual Technical Conference (ATC 2014)

• Q. Ho, J. Cipar, H. Cui, J. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger, and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server, Advances in Neural Information Processing Systems 26 (NIPS 2013)

• J. Cipar, Q. Ho, J. Kim, S. Lee, G. R. Ganger, G. Gibson, K. Keeton, and E. P. Xing, Solving the Straggler Problem with Bounded Staleness, The 14th Workshop on Hot Topics in Operating Systems (HotOS XIV 2013)

• W. Dai, J. Wei, X. Zheng, J. Kim, S. Lee, J. Yin, Q. Ho, and E. P. Xing, Petuum: A Framework for Iterative-Convergent Distributed ML, Advances in Neural Information Processing Systems 26, Big Learning Workshop (NIPS 2013 Big Learning Workshop)

• S. Lee and E. P. Xing, Leveraging Input and Output Structures For Joint Mapping of Epistatic and Marginal eQTLs, Proceedings of the 20th Annual International Conference on Intelligent Systems in Molecular Biology; Bioinformatics (ISMB 2012)

• S. Lee, J. Zhu, and E. P. Xing, Adaptive Multi-Task Lasso: with Application to eQTL Detection, Advances in Neural Information Processing Systems 23 (NIPS 2010)

• S. Lee, E. P. Xing and M. Brudno, MoGUL: Detecting Common Insertions and Deletions in a Population, 14th International Conference on Research in Computational Molecular Biology (RECOMB 2010)

• S. Lee and S. Choi, Ensembles of Landmark Multidimensional Scaling, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009)

• S. Lee, E. Cheran and M. Brudno, A Robust Framework for Detecting Structural Variations in a Genome, Proceedings of the 16th Annual International Conference on Intelligent Systems for Molecular Biology; Bioinformatics (ISMB 2008)

• S. Lee, I. Jeong and S. Choi, Dynamically Weighted Hidden Markov Model for Spam Deobfuscation, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2007)

• S. Lee and I. Jeong, SDD: high performance code clone detection system for large scale source code, Companion to the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications (OOPSLA 2005)

Manuscripts

• S. Lee and E. P. Xing, Screening Rules for Overlapping Group Lasso, Manuscript, arXiv:arXiv:1410.6880

• S. Lee and E. P. Xing, Structured Input-Output Lasso, with Application to eQTL Mapping, and a Thresholding Algorithm for Fast Estimation, Manuscript, arXiv:1205.1989

• W. Dai, J. Wei, X. Zheng, J. Kim, S. Lee, J. Yin, Q. Ho, and E. P. Xing, Petuum: A Framework for Iterative-Convergent Distributed ML, Manuscript, arXiv:1312.7651v1

• S. Lee and E. P. Xing, Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity, Manuscript, arXiv:1208.3014