Curriculum Vitae

YAN LIU

Email: yanliu@cs.cmu.edu

Tel: (412) 268-8492

Fax: (412)268-6298

Homepage: http://www.cs.cmu.edu/~yanliu

     

RESEARCH INTEREST

·   Statistical machine learning, graphical models

·   Computational biology

·   Information retrieval, especially text classification and information extraction

EDUCATION

·   PhD Candidate, Computer Science, Aug 2001 ~ Aug 2006 (Expected), Advisor: Jaime Carbonell
Language Technology Institute, School of Computer Science, Carnegie Mellon University, PA, US

PhD Thesis: Conditional graphical models for protein structure prediction

·   M.Sc. Computer Science, May 2004
Language Technology Institute, School of Computer Science, Carnegie Mellon University, PA, US. GPA:  4.03 (4.0 for A)

·    B.Sc. Computer Science, Jul. 2001
 Computer Science and Technology Department, Peking University, Beijing, P.R.China .

PUBLICATIONS

Journal Papers

• Yan Liu, Jaime Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan.

 Comparison of Probabilistic Combination Methods for Protein Secondary Structure Prediction. [pdf]

 Bioinformatics. 2004 Nov 22;20(17):3099-107.

 

• Yan Liu, Jaime Carbonell, Peter Weigele, Vanathi Gopalakrishnan.

Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs).

To appear in Journal of Computational Biology.

Conference Papers

• Yan Liu, Eric Xing, Jaime Carbonell.

  Predicting Protein Folds with Structural Repeats Using a Chain Graph Model. [pdf]

  To appear in international conference on Machine Learning  (ICML’05), 2005.

 

• Yan Liu, Jaime Carbonell, Peter Weigele, Vanathi Gopalakrishnan.

Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. [pdf]

ACM International conference on Research in Computational Molecular Biology (RECOMB’05), 2005.

 

• Yan Liu, Jaime Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan.

  Context Sensitive Vocabulary and Its Application in Protein Secondary Structure Prediction. [pdf]

 ACM International Conference on Research and Development in Information Retrieval (SIGIR'04), 2004.

 

• John Lafferty, Xiaojin Zhu, Yan Liu.

Kernel Conditional Random Fields: Representation and Clique Selection. [pdf]

The Twenty-First International Conference on Machine Learning (ICML'04), 2004.

 

• Yan Liu, Jaime Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan.

Prediction of Parallel and Antiparallel ί-sheets using Conditional Random Fields.
Biological Language Conference (BLC'03), 2003.

 

• Yan Liu, Jaime Carbonell and Rong Jin.

A New Pairwise Ensemble Approach for Text Classification. [pdf]

The Fourteenth European Conference on Machine Learning (ECML'03), 2003.

 

• Rong Jin, Yan Liu, Si Luo, Jaime Carbonell and Alex Hauptmann.

 A New Boosting Algorithm Using Input-Dependent Regularizer. [pdf]

The Twentieth International Conference on Machine Learning (ICML'03), 2003

 

• Rong Yan, Yan Liu, Rong Jin and Alex Hauptmann.

On Predicting Rare Class with SVM Ensemble in Scene Classification. [pdf]

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), Hong Kong, China, April 6-10, 2003

 

• Yan Liu, Yiming Yang and Jaime Carbonell.

Boosting to Correct the Inductive Bias for Text Classification. [pdf]

ACM International Conference on Information and Knowledge Management (CIKM’02), Nov 4 - Nov 9, 2002.

Technical report

• John Lafferty, Yan Liu and Xiaojin Zhu.

 Kernel Conditional Random Fields: Representation, Clique Selection, and Semi-Supervised Learning.

 CMU technical report CMU-CS-04-115, 2004.

 

RESEARCH EXPERIENCE

 

  • Research Assistant, Carnegie Mellon University, Mar. 2003 – Present

Conditional graphical models for protein secondary structure prediction and protein fold recognition;

Ensemble models for text classification and genre classification;

 

  • Summer Intern, Accenture Technology Lab, Jun, 2005 – Aug, 2005

Statistical models for information extraction

 

  • Research Assistant, Peking University, Artificial Intelligence Lab, Jul. 2000 – Jul. 2001

Fast indexing algorithms for high-dimensional data.

 

TEACHING EXPERIENCE

 

  • Teaching Assistant, Carnegie Mellon University, Spring 2004

11-761: Language and Statistics

 

  • Teaching Assistant, Carnegie Mellon University, Fall 2003

20-760: Web-Based Information Architectures

 

  • Teaching Assistant, Carnegie Mellon University West Campus, Fall 2002, Summer 2003

Web-Based Information Architectures

  

AWARDS & FELLOWSHIPS

 

·       Aug.2001 – Present, Carnegie Mellon Research Fellowship

 

·       Jul. 2001, Graduate with Honor, Peking University

 

·       Jun. 2001, Best undergraduate thesis, computer science and technology department, Peking University

 

·       Sep 1997 – Jul. 2001, various scholarships and awards including Guangcai Scholarship, Legend Scholarship, Guanghua Scholarship, et al.

 

 

PROFESSIONAL SERVICES

 

Referee for Journals

 

·       Journal of Artificial Intelligence Research (JAIR)

·       Information Processing & Management (IP&M)

·       Bioinformatics.

·       FEBS letters

 

PROFICIENCY

 

  • Proficiency in C, C++, Java, JSP, Pascal, MATLAB, Perl, LaTeX;
  • Proficiency in Windows, Linux, X-Windows