Seunghak Lee (이승학)
Recently, I joined 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.
8114 Gates-Hillman Center (GHC)
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh PA 15213
August 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.
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
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)
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)
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)