1. Yunting Xiao
186 Claremont Avenue, Apt.1N·New York, NY 10027·1-347-449-2767
Email: yunting.xiao@gmail.com, yx2167@columbia.edu
Education
Columbia University, Fu Foundation School of Engineering and Applied Science New York, NY
MS in Electrical Engineering, GPA 3.7/4.0 Expected Dec 2011
Relevant Coursework:
Advanced Machine Learning Large Scale Machine Learning
Machine Learning for Natural Language Processing Data Mining
Network Science Computer Graphic
Sparse Signal Modeling Visual Search Engine
Advanced Projects in Multimedia Lab Advanced Database Systems
Beijing University of Posts and Telecommunication, Information & Communication Beijing, China
BS in Telecommunication Engineering, GPA 3.5/4.0 July 2010
Relevant Coursework:
Information Theory Digital Signal Processing
Communication Theory Digital Image Processing
Signal and System C++
Computer Network Data Structure
Project Experience
Columbia University New York, NY
“String-to-Dependency-Tree Machine Translation” Spring, 2011
• Based on the existing Joshua Decoder, implemented a string to dependency tree statistical machine translator
• Modified the translation model building part and wrote the language model building part
“Clothes Visual Search and Recommendation System” Spring, 2011
• Applied ‘bag of words’ image representation in clothes matching, and recommendation based on common sense
• Got image datasets by Zappos API, and provided the final system with the help of multiple platforms including
Matlab, C sharp, MySQL
“The Survey on Gene Expression Prediction” Spring, 2011
• Explored different group of methods: direct group, k-nearest-neighbor, Orthogonal Matching Pursuit, LARS to do
gene expression prediction; focused on sparse signal modeling methods
• Repeated the methods in given top performance papers and found some better methods
“B-matching Approximation for Semi-supervised learning” Fall, 2010
• Extracted Twitter data using twitter API, using the extracted 13,628 people information to build network
• Applied advanced machine learning algorithm to improve semi-supervised learning result
Experience
IBM Thomas J. Watson Research Center Yorktown Heights, NY
“SmallBule.com Website building” Spring, 2011
• Extracted experts information from their homepage, such as e-mail, phone number and interests
• Using Python to do semantic parsing and information extraction
• Applied Tree CRF algorithm to improve the annotation performance
Technical Skills
Programming Languages: C++, Python, Java, C#, and MATLAB
Applications: MySQL, Adobe Photoshop CS4, Microsoft Office, Microsoft Publisher,
3D Max, After Effects CS4, Adobe Premiere CS4, Open Office, LaTeX
Operating System: Windows 7, Mac OS X
Research & Development: Machine Learning (Natural Language Processing, Visual Search Engine,
Large Scale Datasets), Computer Software Design (MFC Programming, OpenGL),
Image Processing (OpenCV)