 | Sangkyun Lee Contact: 4352 Computer Sciences & Statistics 1210 West Dayton Street Madison, WI 53706  |
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I’m currently a postdoc at TU Dortmund in Germany!
I received my Ph.D. degree in the Computer Sciences department of the Univ. of Wisconsin, Madison in July 2011. When I was a kid, I was fascinated with Astronomy. It was an unforgettable event taking pictures of the comet Halley. The amusement of understanding the physics of the space had led me to fall in love with Mathematics.
Optimization is a study of the structures of spaces, seeking for efficiency in complications. Among the amazing people in the operations research group in the UW-Madison, I’m primarily working with Prof. Stephen J. Wright.
Research Interests
- Numerical optimization algorithms for large-scale regularization problems.
- Sparse models in machine learning, statistics, and signal/image processing.
Education
- Ph.D., Computer Sciences, University of Wisconsin-Madison, 2011 (focus on Optimization/Statistics).
- M.S., Computer Sciences, University of Wisconsin-Madison, 2008.
- M.S., Electrical Engineering and Computer Science, Seoul National University, 2005.
- B.S., Computer Science and Engineering, Seoul National University, 2003.
Publications
Journal Papers
- Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation, Nico Piatkowski, Sangkyun Lee and Katharina Morik, Machine Learning, 2013 [pdf] *Best paper award in the journal track in ECML/PKDD 2013 (acceptance rate: 7%)
- Manifold Identification in Dual Averaging Methods for Regularized Stochastic Online Learning, Sangkyun Lee and Stephen J. Wright, Journal of Machine Learning Research (JMLR), 2012 [pdf]
Conference Papers
- Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-Limited Sensor Networks, Sangkyun Lee and Christian Pölitz, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2014 [pdf]
- Integer Approximation of Undirected Graphical Models, Nico Piatkowski, Sangkyun Lee, and Katharina Morik, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2014 [pdf]
- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines, Sangkyun Lee and Stephen J. Wright, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2012. [pdf] [code]
- Improving Confidence of Dual Averaging Stochastic Online Learning via Aggregation, Sangkyun Lee, German Conference on Artificial Intelligence (KI), 2012. [pdf]
- Separable Approximate Optimization of Support Vector Machines for Distributed Sensing, Sangkyun Lee, Marco Stolpe, and Katharina Morik, European Conferences on Machine Learning (ECML), 2012. [pdf]
- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines, Sangkyun Lee and Stephen J. Wright, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2012. [pdf] [code]
- Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning, Sangkyun Lee and Stephen J. Wright, International Conference on Machine Learning (ICML), 2011. [pdf] [poster]
- Decomposition Algorithms for Training Large-scale Semiparametric Support Vector Machines, Sangkyun Lee and Stephen J. Wright, European Conferences on Machine Learning (ECML), 2009. [pdf] [poster]
Workshop Papers / Conference Talks
- Spatio-Temporal Models For Sustainability, Nico Piatkowski, Sangkyun Lee and Katharina Morik, SustKDD Workshop in ACM Conference on Knowledge Discovery and Data Mining (KDD), 2012. [pdf]
- Scalable Sochastic Gradient Descent with Improved Confidence, Sangkyun Lee and Christian Bockermann, Big Learning workshop in Neural Information Processing Systems (NIPS), 2012. [pdf]
- Signal Processing Algorithms on Graphical Processing Units, Sangkyun Lee and Stephen J. Wright, INFORMS Annual Meeting, Invited Talk, 2009. [slides]
- Decomposition and Stochastic Subgradient Algorithms for Support Vector Machines, Sangkyun Lee and Stephen J. Wright, International Symposium on Mathematical Programming (ISMP), 2009. [slides]
Book Chapters
- Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure, Sangkyun Lee, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer, 2014. [pdf]
- Stochastic Subgradient Estimation Training for Support Vector Machines, Sangkyun Lee and Stephen J. Wright, Mathematical Methodologies in Pattern Recognition and Machine Learning, 2013. [pdf]
- Combining Information-based Supervised and Unsupervised Feature Selection, Sangkyun Lee, S.-J. Lee and B.-T. Zhang, Feature Extraction: Foundations and Applications, Springer, 2006. [Amazon]
Technical Reports
- Approximate Stochastic Subgradient Estimation Training for Support Vector Machines, Sangkyun Lee and Stephen J. Wright, Nov 2011. [arxiv.org]
- Manifold Identification in Dual Averaging Methods for Regularized Stochastic Online Learning, Sangkyun Lee and Stephen J. Wright, University of Wisconsin-Madison, July 2011 (supercedes April 2011 ver.). [pdf]
- Implementing Algorithms for Signal and Image Reconstruction on Graphical Processing Units, Sangkyun Lee and Stephen J. Wright, University of Wisconsin-Madison, 2008. [pdf] [code]
Ph.D. Thesis
- Optimization Methods for Regularized Convex Formulations in Machine Learning, Sangkyun Lee, 2011. [pdf]
Awards
- Samsung Scholarship, Samsung Foundation of Culture, 2005-2010.
- RA Fellowship, Dept. of Computer Sciences, University of Wisconsin-Madison, 2006.
- Graduate summa cum laude, B.S. in Computer Science and Engineering, Seoul National University, 2003.