Seunghak Lee (이승학)

Recently, I moved to Human Longevity, Inc, as a research scientist. Prior to that, I was a project scientist in the Machine Learning Department at Carnegie Mellon University, working with Prof. Eric P. Xing. I received my Ph.D. in Computer Science Department at Carnegie Mellon University, M.Sc. in Computer Science at the University of Toronto, and B.S. in Chemistry and Computer Science and Engineering at POSTECH.

My research interests include computational biology and machine learning. I am interested in integrative approaches to the analysis of genetic and biomedical datasets, genome-wide association studies, visual analytics, distributed optimization, and large-scale machine learning algorithms and systems.

News

• Aug 2015: I joined Human Longevity, Inc as a research scientist.

• Mar 2015: I joined Machine Learning Department at CMU as a project scientist.

• Feb 2015: I obtained my Ph.D. in Computer Science Department at CMU.

Software

• Petuum: large-scale distributed machine learning framework. This software contains our works for parameter server and scheduler.

Selected Publication

Conference Papers

• J. Kim, Q. Ho, S. Lee, X. Zheng, W. Dai, G. Gibson, E. P. Xing, STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning, To appear in The European Conference on Computer Systems (EuroSys 2016)

• 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, 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, 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)

Journal Papers

• E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, Y. Yu, Petuum: A New Platform for Distributed Machine Learning on Big Data, IEEE Transactions on Big Data, 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, Journal of Computational Biology (RECOMB 2015 Special Issue), 2015

• 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

Books

• E. P. Xing, M. Kolar, S. Kim, X. Chen, S. Lee, Chapter 4: High-Dimensional Sparse Structured Input-Output Models, with Applications to GWAS in Practical Applications of Sparse Modeling (MIT Press)

Selected 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