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Biography

I am a 4th year Ph.D. student in the Machine Learning Department within the School of Computer Science at Carnegie Mellon University. I am working with Prof. Jaime G. Carbonell on fast and scalable algorithms sparse learning problems and active learning. I am awarded an IBM Ph.D. Fellowship for the academic year 2011~2012. Thanks IBM!

Thesis Proposal: Learning with Sparsity: Structures, Optimization and Applications

Research Statement: Download

Before that, I obtained my master of science in Industry Administration (Operations Research) from Tepper School of Business at Carnegie Mellon University at ACO (algorithms, combinatorics and optimization) program. My master work is advised by Prof. Manuel Blum on predicting integer sequences from Sloan.

I obtained my Bachelor in Computer Science in Special Class for the Gifted Young of China from Xi'an Jiaotong University at the age of 20.

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Research Interests

 Machine Learning: Structured Sparse Learning for Ultra-high Dimensional Data (e.g. learning sparse dynamic networks), Nonparametric Sparse Learning, Sparse Learning for Text Data Analysis (Ranking, Dimensional Reduction, Topic Learning), Time Evolving Recommendation System

   Optimization: Non-smooth ''Optimal" First-order Methods, Stochastic Optimization for Online Learning Problems

 Combinatorics : Predicting integer sequences on Sloan (On-Line Encyclopedia of Integer Sequences) via Combinatorial approaches.

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Publications

 

Group Sparse Additive Models

Junming Yin, Xi Chen,  Eric Xing

In International Conference on Machine Learning (ICML) 2012

 

Structured Sparse Canonical Correlation Analysis

Xi Chen, Han Liu and Jaime G. Carbonell

In International Conference on Artificial Intelligence and Statistics (AISTATS) 2012 (Oral Presentation) (PDF)

Code: Group Structured Sparse CCA (download)

 

Adaptive Multi-task Sparse Learning with an Application to fMRI Study

Xi Chen, Jingrui He, Rick Lawrence and Jaime G. Carbonell

In SIAM International Conference on Data Mining (SDM)  2012 (Oral Presentation) (PDF)

 

A Sparsity Preserving Stochastic Gradient Method for Composite Optimization

Qihang Lin, Xi Chen, Javier Pena Manuscript (2011): http://www.optimization-online.org/DB_HTML/2011/04/3009.html

Direct Robust Matrix Factorization for Anomaly Detection

Xiong Liang, Xi Chen, Jeff Schneider International Conference on Data Mining (ICDM) 2011  (PDF)

Smoothing Proximal Gradient Method for General Structured Sparse Learning

Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell and Eric P. Xing

Uncertainty in Artificial Intelligence (UAI) 2011 (PDF)

Full Version: Annals of Applied Statistics (to be appeared) (PDF) Code: SPG for Uni-response Overlapping group lasso and Graph-guided Fused Lasso (download)           SPG for Multi-task Graph-guided Fused Lasso (download)

Multi-target QSAR Modelling in the Analysis and Design of HIV-HCV Co-Inhibitors : An In-silico Study Qi Liu, Han Zhou, Lin Liu, Xi Chen, Ruixin Zhu and Zhiwei Cao BMC Bioinformatics, 12:294, 2011 (PDF)   A Smoothing Stochastic Gradient Method for Composite Optimization Qihang Lin, Xi Chen, Javier Pena Manuscript (2010): arXiv:1008.5204v2  http://arxiv.org/abs/1008.5204

Sparse Latent Semantic Analysis

Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin and Jaime G. Carbonell SIAM International Conference on Data Mining (SDM) 2011 (PDF) Code: sparseLSA (download)

Learning Preferences with Millions of Parameters by Enforcing Sparsity

Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin and Jaime G. Carbonell

International Conference on Data Mining (ICDM) 2010 (PDF)

Graph-Valued Regression

Han Liu, Xi Chen, John Lafferty and Larry Wasserman

In Advances in Neural Information Processing Systems (NIPS), 23, 2010 (Spotlight) (PDF)

Multivariate Dyadic Regression Trees for Sparse Learning Problems

Han Liu and Xi Chen

In Advances in Neural Information Processing Systems (NIPS), 23, 2010.  (PDF)

Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis

Xi Chen, Yan Liu, Han Liu and Jaime G. Carbonell

Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI), 2010 (PDF)

 

Time-evolving collaborative filtering Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider and Jaime Carbonell SIAM International Conference on Data Mining (SDM) 2010 (PDF)

Accelerated Gradient Method for Multi-Task Sparse Learning Problem

Xi Chen, Weike Pan, James Kwok and Jaime Carbonell

International Conference on Data Mining (ICDM) 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)

Canonical Forms under Similarity for Involutory Matrices over the Ring of Integers Modulo 2^m

Xi Chen

Mathematics Technology & Applied Science, April 2006 (PDF)

 

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Honors

 

IBM Ph.D. Fellowship (09/2011)

 

American Statistical Association (ASA) Stat. Computing and Stat. Graphics Student Paper Award (01 / 2010)

 

Chiang Chen Overseas Graduate Fellowship (04 / 2007)

(Awarded $ 50,000  to ten best students in China for overseas study)

 

The Tenth Place of the ACM Asia Programming Contest ( ACM / ICPC ), Beijing Site, Final Round (11 / 2004)
(International Collegiate Programming Contest sponsored by Association for Computer Machinery)

Semi-finalist in the Algorithm Invitational of the 2004 Microsoft Imagine Cup Competition (05/2004)

(Sponsored by Microsoft, 200 students all over the world received this award)

IBM China Excellent Student (08 / 2005)
(Sponsored by IBM, awarded to 80 students in China based on academic merit)

Special Excellent Student of XJTU (12 / 2004 & 12 / 2005)

(Awarded to only 10 students out of 5,000 undergraduate students at XJTU)

Peng Kang Scholarship (12 / 2004)
(Awarded to the top 1% of the grade )

First Class Scholarship (12 / 2003)
(Awarded to the top one student in Special Class for the Gifted Young)

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Research Internship

 

IBM Thomas J. Watson Research Center            (June. 11 ~ Aug. 11)

 

NEC Lab America (Machine Learning Group)      (June. 10 ~ Sept. 10)

 

Research Assistant for Prof. James Kwok              (May. 09 ~ Aug. 09)

Hongkong University of Science and Technology

 

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Invited Talks

Proximal Gradient Descent for Structured Sparse Learning Problems

   IBM T.J. Watson (June 2010); 

   CS Dept. at University of British Columbia (July 2010);

   CS Dept. at Princeton University (Aug 2010)  

 

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Teaching Experience

Teaching Assistant for Machine Learning (10701)  (Spring 2011)  

    Instructor: Tom Mitchell

Teaching Assistant for Statistical Machine Learning (10702)  (Spring 2010)  

    Instructor: John Lafferty and Larry Wasserman

 

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Graduate Coursework

 

• 36-707 (F09): Regression Analysis (A+), by Kathryn Roeder

• 15-826 (S09): Multimedia Databases and Data Mining (A+), by Christos Faloutsos                       

• 10-702 (S09): Statistical Machine Learning (A), by John Lafferty & Larry Wasserman

• 10-705 (F08): Intermediate Statistics (A+), by Matthew T. Harrison

• 15-853 (F08): Algorithm in the Real World (A+), by Guy E Blelloch

• 11-765 (F08): Active Learning Seminar (A), by Jaime G. Carbonell

• 11-701 (S07): Machine Learning (A), by Eric Xing

• 48-761 (S07): Convex Polytopes (A+), by Egon Balas

• 47-831 (S07): Advanced Integer Programming (A), by Gerard Cornuejols

• 47-830 (S07): Integer Programming (A), by Gerard Cornuejols

• 21-701 (F07): Discrete Mathematics (A), by Thomas Bohman

• 47-836 (F07): Networks and Matchings (A+), by Egon Balas

• 47-835 (F07): Graph Theory (A+), by Egon Balas

• 47-834 (F07): Linear Programming (A), by John Hooker

 

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