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I will be joining the Departments of Computer Science & Engineering and Genome Sciences at the University of Washington as an Assistant Professor. I recently graduated from Stanford University. My thesis advisor was Prof Daphne Koller in Computer Science at Stanford.
Computational Biology
Developing computational methods for understanding the genetic basis of complex traits.
Machine Learning
Developing machine algorithms for general applications.
My long-term goal is to understand the genetic basis of complex traits by developing novel machine learning algorithms that can infer meaningful biological mechanisms from different pieces of biological evidence. The recent advent of high-throughput genotyping methods has enabled retrieval of an individual's sequence information on a genome-wide scale. However, the complexity of cellular mechanisms induced by sequence variations still makes it difficult to infer the causal relationships between the sequence variations and a particular trait. My goal is to address this challenge by developing effective machine learning algorithms that can translate sophisticated biological processes into robust statistical models; can infer their underlying mechanisms from high-dimensional, sparsely sampled data; and can learn such models from data efficiently. I believe that these approaches can enable more comprehensive understanding of disease genetics, which could lead to the realization of personalized medicine. Many of the machine learning techniques are generally applicable to a wide-ranging set of applications.
A pluripotency signature predicts histological transformation and influences survival in follicular lymphoma patients
Andrew J. Gentles*, Ash A. Alizadeh*, Su-In Lee, June H. Myklebust, Babak Shahbaba, Catherine M. Shachaf, Ronald Levy, Daphne Koller, Sylvia K. Plevritis. Under Review in Blood
Learning a Prior on Regulatory Potential from eQTL Data
Su-In Lee, Aimee M. Dudley, David Drubin, Pamela A. Silver, Nevan J. Krogan, Dana Pe'er and Daphne Koller. PLoS Genet 5(1): e1000358. doi:10.1371/journal.pgen.1000358
[Maintext][SupportingInformation][EndNote][Software]
Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks
Su-In Lee, Vassil Chatalbashev, David Vickrey and Daphne Koller. In Proceedings of International Conference on Machine Learning (ICML 2007), Corvallis, OR, June 2007.
[PDF]
Efficient Structure Learning of Markov Networks using L1-Regularization.
Su-In Lee, Varun Ganapathi and Daphne Koller. In Proceedings of Neural Information Processing Systems (NIPS 19, 2007), Vancouver, British Columbia, December 2006.
[PDF]
Identifying Regulatory Mechanisms using Individual Variation Reveals Key Role for Chromatin Modification.
Su-In Lee*, Dana Pe'er*, Aimee M. Dudley, George M. Church and Daphne Koller. Proceedings of the National Academy of Sciences (PNAS), September 2006, 103: 14062-14067.
[Maintext][SupportingInformation][EndNote]
Efficient L1 Regularized Logistic Regression.
Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), Boston, MA, USA, July 2006.
Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae.
James E. Galagan, Sarah E. Calvo, Christina Cuomo, Li-Jun Ma, Jennifer R. Wortman, Serafim Batzoglou, Su-In Lee, et al. Nature, December 2005, 438(7071): 1105-1115.
ICA-based Clustering of Genes from Microarray Expression Data.
Su-In Lee and Serafim Batzoglou. In Proceedings of Neural Information Processing Systems (NIPS 16, 2004), Vancouver, British Columbia, December 2003.
[PS][PDF][Bibtex][Software-UnderConstruction]
Application of Independent Component Analysis to Microarrays.
Su-In Lee and Serafim Batzoglou. Genome Biology, October 2003, 4(11):R76. - Highly Accessed
[Link][PDF][EndNote][Software-UnderConstruction]
Top-Down Attention Control at Feature Space for Robust Pattern Recognition.
Su-In Lee and Soo-Young Lee. In Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision (BMCV2000), May 2000.
[BibTex]
Biologically Inspired Neural Network Approach using Feature Extraction and Top-Down Selective Attention for Robust Optical Character Recognition.
Su-In Lee and Soo-Young Lee. In Proceedings of Humantech Paper Competition held by Samsung Electronics, Inc., Jan 2000. - Gold prize (the first prize)
GenViewer In preparation
Lirnet Lee et al., Learning a Prior on Regulatory Potential from eQTL Data, PLoS Genet 2009.
IRLS-LARS Lee et al., Efficient L1 Regularized Logistic Regression, AAAI 2006.