Han Liu
| Email: hanliu@cs.cmu.edu
|
Gates Hillman Complex 8008, Carnegie Mellon University , 5000 Forbes Ave. Pittsburgh, PA 15213 , Phone: 412. 268. 8699 |
Research
I am a 4th-year PhD student in the Machine Learning Department within the School of Computer Science at Carnegie Mellon University. I am in the Joint PhD program in Machine Learning and Statistics. My advisors are John Lafferty and Larry Wasserman. I obtained my Msc in Statistics and Machine Learning at Carnegie Mellon University in 2007 and another Msc in Computer Science at University of Toronto in 2005.
My main research interest is nonparametric inference for high-dimensional massive datasets. I am thankful for support from Google for my research in 2009-2011 through the Google PhD Fellowship Program.
My theoretical research interests have focused on developing scalable methods that are powerful enough to capture the subtleties arising in the modern data. My dissertation proposes solutions to the following tasks:
- (T1) Supervised predictive modeling: examples include regression, classification, and other multivariate analysis methods;
- (T2) Unsupervised exploratory data analysis: examples include density estimation, clustering, and large-scale structure learning;
- (T3) More complicated learning scenarios: examples include multi-task and multi-response learning, temporal and spatial adaptive learning.
Useful tools involved in my research include asymptotic statistics, stochastic calculus, empirical process theory, concentration of measure inequalities, functional and convex analysis, sub-differential calculus and complexity theory, etc.
My applied research interests have ranged from data visualization to various problems in scientific data mining, and image processing. Examples include gene microarray data, protein tandem mass spectrometry data, astrophysics galaxy spectra, fMRI imaging data, query logs and text corpus, robot path planning, etc.
- 10-725 (S08) Optimization Theory (graduate course), at the Machine Learning Department, Carnegie Mellon Universitys;
- 36-625 (S07) Probability and Statistics (graduate course), at the Statistics Department, Carnegie Mellon Universitys;
- 36-711 (F07) Statistical Computing (graduate course), at the Statistics Department, Carnegie Mellon Universitys;
- 36-217 (S06, F05) Stochastic Process (ungraduate course), at the Statistics Department, Carnegie Mellon Universitys;
- CS-364 (F04) Computational Complexity (undergraduate course), at the Department of Computer Science, University of Toronto;
- CS-263 (S04, F03) Theory of Computation (undergraduate course), at the Department of Computer Science, University of Toronto.
- The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Han Liu, John Lafferty, and Larry Wasserman
Journal of Machine Learning Research (JMLR), to appear, 2009.
PDF - Nonparametric Greedy Algorithm for the Sparse Learning Problems
Han Liu and Xi Chen
In Advances in Neural Information Processing Systems (NIPS), 22, 2009.
PDF - On the Estimation
and Variable Selection Consistency of the Sum of q-norm Regularized
Regression
Han Liu and Jian Zhang
Technical Report, Department of Statistics, Carnegie Mellon University (CMU-STAT-TR-86), 2009.
PDF - Some
Two-step Procedures for Variable Selection in High-dimensional Linear
Regression
Jian Zhang, Jessie Jeng, and Han Liu
Technical Report, Department of Statistics, Purdue University (Purdue-STAT-TR-08-05), 2009.
PDF - Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery
Han Liu, Mark Palatucci, and Jian Zhang
The Twenty-sixth International Conference on Machine Learning (ICML), 2009.
PDF (Winner of the Best Student Paper Award and Best Overall Paper Honorable Mention) - Sparse Additive Models
Pradeep Ravikumar, John Lafferty, Han Liu, Larry Wasserman
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSSB), 2009.
PDF - On the Estimation Consistency of the Group Lasso and its Applications
Han Liu and Jian Zhang
In Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), (JMLR-WCP)5:376-383, 2009
PDF (Best Paper Award Nominee) - Nonparametric Regression and Classification with Joint Sparsity Constraints
Han Liu, John Lafferty, and Larry Wasserman
In Advances in Neural Information Processing Systems (NIPS), 21, 2008.
PDF - Discovering a Semantic Basis of Neural Activity using Simultaneous Sparse Approximation
Mark Palatucci, Tom Mitchell, and Han Liu
In Sparse Optimization and Variable Selection Workshop (ICML), 2008.
PDF - SpAM: Sparse Additive Models
Pradeep Ravikumar, Han Liu, John Lafferty and Larry Wasserman
In Advances in Neural Information Processing Systems (NIPS), 20, 2007.
PDF - Sparse Nonparametric Density Estimation in High Dimensions using the Rodeo
Han Liu, John Lafferty, and Larry Wasserman
Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS),JMLR-WCP vol 2:283-290, 2007.
PDF - Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data
Anthony Bonner and Han Liu
Sixth SIAM International Conference on Data Mining (SDM), 2006.
PDF - A Framework for Efficient Association Rule Mining in XML Data
Ji Zhang, Han Liu, Tok Wang Ling, Robert M.Bruckner, and A. Min Tjoa
Journal of Database Management (JDM). pp19-40 Volume 17 Issue 3, 2006.
PDF - Canonical Correlation, an Approximation, and the Prediction of Protein Abundance
Anthony Bonner and Han Liu
Proceedings of the Eighth Workshop on Mining Scientific and Engineering Datasets (MSD), 2005.
PDF - A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation
Yong-Ji Wang, Matthew Cartmell, Qiu-Ming Tao, Han Liu
Journal of Computer Science and Technology (JCST), Volume 20(6): 774-787, 2005.
PDF - D-GridMST: Clustering Large Distributed Spatial Databases
Ji Zhang and Han Liu
Book Chapter of Classification and Clustering for Knowledge Discovery. Springer-Verlag Publisher, ISBN 978-3-540-26073-8, 2005.
PDF - Dimension Reduction of Microarray Data with Penalized Independent Component Analysis
Han Liu and Rafal Kustra
The NIPS workshop on New Problems and Methods in Computational Biology (NIPS), 2005.
PDF - Predicting Protein Levels from Tandem Mass Spectrometry Data
Anthony Bonner and Han Liu
The NIPS workshop on New Problems and Methods in Computational Biology (NIPS), 2005.
PDF - Comparison of Discrimination Methods for Peptide Classification in Tandem Mass Spectrometry
Anthony Bonner and Han Liu
IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), 2004.
PDF - Modeling Protein Tandem Mass Spectrometry Data with an Extended Linear Regression Strategy
Han Liu, Anthony Bonner, and Andrew Emily
The Twenty-sixth Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), 2004.
PDF - An Efficient Method to Estimate Labeled Sample Size for Transductive LDA( QDA / MDA) based on the Bayes Risk
Han Liu, Xiaobin Yuan, Qianying Tang, and Rafal Kustra
The Fifteenth European Conference on Machine Learning (ECML'04), 2004.
PDF - A Realistic Method for Real-time Obstacle Avoidance without the Calculation of Cspace Obstacles
Han Liu and Yongji Wang
IEEE International Conference on Robotics and Automaton (IEEE RAM), 2004.
PDF - A Real-time Robot Path Planning Approach without the Computation of Cspace Obstacles
Yongji Wang, Han Liu, Qing Wang, Mingshu Li, Jinhui Zhou and M. Cartmell
Journal of Robotica. pp173-187 Volume 22 Issue 2, 2004.
PDF
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21-651 (F09): General topology, by Giovanni Leoni
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15-826 (S09): Multimedia databases and data mining, by Christos Faloutsos
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21-640 (S09): Introduction to Functional analysis (I), by Willam Hrusa
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36-756 (F08): Advanced statistical theory (III), by Jiashun Jin
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21-880 (F08): Advanced stochastic calculus (I), by Kavita Ramanan
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36-900 (S08): Contemporary frontier of high dimensional inference, by Jiashun Jin
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36-756 (F07): Advanced statistical theory (II), by Jong soo Lee
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36-835 (F07): Foundation of statistics seminar, by Stephen E. Fienberg
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36-754 (S07): Advanced probability (II), by Cosma Shalizi
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36-757 (S07): Advanced data analysis (II), by Anthony Brockwell
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36-755 (S07): Advanced statistical theory (I), by Alessandro Rinaldo
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15-853 (F06): Algorithm in the real world, by Guy E Blelloch and Anupam Gupta
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36-757 (F06): Advanced data analysis (I), by William Eddy
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36-752 (F06): Advanced probability (I), by Chad Schafer
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36-724 (S06): Bayesian inference and computational statistics, by Brian W.Junker
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10-702 (S06): Statistical machine learning, by Larry Wasserman and John Lafferty
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36-703 (S06): Intermediate probability, by Chris Genovese
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36-708 (S06): Linear models and experimental design, by Pantelis Vlachos
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36-705 (F05): Intermediate statistics, by Kathryn Roeder
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36-707 (F05): Regression analysis, by Larry Wasserman
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CS-598 (S05): Advanced information retrieval, by Chengxiang Zhai
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CS-512 (S05): Data mining: principles and algorithms, by Jiawei Han
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CS-591 (S05): Bioinformatics seminar, by Jiawei Han
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CS-591 (S05): Data and information system seminar, by Jiawei Han
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CS2221(F04): Theory of distributed computing, by Vassos Hadzilacos and Sam Toueg
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CS2541(F04): Advanced topics in Bayesian learning, by Radford A. Neal
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CS2509(S04): Database management system, by Anthony J.Bonner
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ST7001(S04): Statistical theory in data mining, by Nancy M.Reid and Rafal Kustra
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CS2401(F03): Computational complexity, by Alan Borodin
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CS2410(F03): Computability and logic, by Stephen A. Cook
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CS2515(F03): Machine learning, by Sam Roweis
- May 2008 - Aug 2008
Joint intern at Microsoft research Redmond and Microsoft Live Search, Redmond, WA
Mentors: Ryen White and Peter Bailey
Project: Long and rare query classification using the interactive logs. - Jan 2005 - May 2005
Research programmer in the automated learning group (ALG) at national center for supercomputing and applications (NCSA) , Champaign, IL
Mentor: Loretta Auvil
Project: Discriminant analysis of the oceanic data. - Aug 2002 - Aug, 2003
Software engineer in the institute of software at Chinese academy of sciences, Beijing, P.R. China
Mentor: Mingshu Li
Project: User-oriented domain-specific requirements elicitation.
|
Gates and Hillman Complex 8008
5000 Forbes Ave.
Email: hanliu@cs.cmu.edu Office Phone: 412. 268. 8699 |
