I received my Ph.D. from the Computer Science Department at Stanford University, advised by Professor Andrew Ng. Starting September 2010, I will join the EECS Department at University of Michigan as an Assistant Professor, and my web presence will shift here. (This page will be redirected to my new webpage.)
My research interests lie in machine learning and its application to a range of perception problems in the fields of artificial intelligence, such as computer vision, robotics, audio recognition, and text processing. I am particularly interested in developing algorithms for automatically learning feature representations from unlabeled data. I am also interested in data mining, probabilistic graphical models, convex optimization, high-dimensional data analysis, and large-scale learning using massive datasets.
An integrated machine learning approach to stroke prediction.
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Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Kuang Chiu, Junling Hu, and Honglak Lee.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2010.
Unsupervised feature learning for audioclassification using convolutional deep belief networks.
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[code]
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Honglak Lee, Yan Largman, Peter Pham, and Andrew Y. Ng.
Advances in Neural Information Processing Systems (NIPS) 22.
Measuring invariances in deep networks.
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[bib]
Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, and Andrew Y. Ng.
Advances in Neural Information Processing Systems (NIPS) 22.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
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[bib]
[code]
[talk video]
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng.
In Proceedings of the Twenth-Sixth International Conference on Machine Learning (ICML), 2009.
Best paper award: Best application paper.
Exponential Family Sparse Coding with Application to Self-taught Learning.
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[bib]
Honglak Lee, Rajat Raina, Alex Teichman, and Andrew Y. Ng.
In Proceedings of the Twenth-First International Joint Conference on Artificial Intelligence (IJCAI-09), 2009.
Sparse deep belief net model for visual area V2.
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[bib]
Honglak Lee, Chaitu Ekanadham, and Andrew Y. Ng.
Advances in Neural Information Processing Systems (NIPS) 20, 2008.
Self-taught learning: Transfer learning from unlabeled data.
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Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y. Ng.
In Proceedings of the Twenty-fourth International Conference on Machine Learning (ICML), 2007.
Efficient sparse coding algorithms.
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[code]
Honglak Lee, Alexis Battle, Rajat Raina, and Andrew Y. Ng.
Advances in Neural Information Processing Systems (NIPS) 19, 2007.
Efficient L1 regularized logistic regression.
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[code]
Su-In Lee, Honglak Lee, Pieter Abbeel, and Andrew Y. Ng.
In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), 2006.
Quadruped robot obstacle negotiation via reinforcement learning.
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[bib]
[videos]
Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, and Andrew Y. Ng.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2006.
A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image.
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[bib]
[experiments]
Erick Delage, Honglak Lee, and Andrew Y. Ng.
In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
Automatic single-image 3d reconstructions of indoor Manhattan world scenes.
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[experiments]
Erick Delage, Honglak Lee, and Andrew Y. Ng.
In Proceedings of the 12th International Symposium of Robotics Research (ISRR), 2005.
Spam deobfuscation using a hidden Markov model.
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[bib]
Honglak Lee and Andrew Y. Ng.
In Proceedings of the Second Conference on Email and Anti-Spam (CEAS), 2005.
Best student paper award.
Journals:
High-throughput identification of transcription start sites, conserved promoter motifs, and predicted regulons.
Patrick T. McGrath, Honglak Lee, Li Zhang, Antonio A. Iniesta, Alison K. Hottes, Meng How Tan, Nathan J. Hillson, Ping Hu, Lucy Shapiro, and Harley H. McAdams.
Nature Biotechnology, 25, pp. 584-592 (2007).
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