Bio

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I am currently a Master student in Intelligent Information Systems at Carnegie Mellon University, from fall 2015. I am interested in natural language processing and machine learning.

Previously, I graduated from Shanghai Jiaotong University with a bachelor's degree in Information Security. I was a core member of Advanced Data And Programming Technology Laboratory directed by Kenny Q. Zhu. My past work mostly focused on extraction and analysis of medical information from traditional documents and social media websites.

During the fifth semester of undergraduate, I studied at Monash University, Australia, as an exchange student. Under the supervision of Professor Geoffrey I. Webb and Dr Ana M. Mart¨ªnez, I worked on parallelization by boost.

 

Chen Ge

Master Student in Intelligent Information Systems (MIIS)

Language Technologies Institute

School of Computer Science

Carnegie Mellon University  

Email: cge1 [at] cs [dot] cmu [dot] edu

Contact

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Email: cge1 [at] cs [dot] cmu [dot] edu

Phone: 412-915-0293

Education

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• Carnegie Mellon University
  from 2015.08 to present
  Master in Intelligent Information Systems

• Shanghai Jiaotong University
  from 2011.09 to 2015.06
  Bachelor of Science in Information Security, 3.75/4.00

• Monash University
  from 2013.07 to 2013.12
  Exchange Undergraduate, Information Technology, 4.0/4.0

Employment

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    AstraZeneca Inc.   
     from 2014.08 to 2015.05
    Software Engineer Intern, Department of R&D Information                                                  

• Directed the text mining project which identified and analysed adverse drug reactions from discussions on Chinese social media.
• Designed four independent web scraping tools for Weibo, haodf.com, xywy.com and a drug forum. Given a drug name, they can automatically extract related texts posted by users.
  
• Implemented algorithms to get top adverse drug reactions and related drugs information within extracted texts.

Publication

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• Dong Xu, Meizhuo Zhang, Tianwan Zhao, Chen Ge, Weiguo Gao, Jia Wei and Kenny Q. Zhu.
  Data-Driven Information Extraction from Chinese Electronic Medical Records. In PLOS ONE, Aug, 2015. [link]

Research

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    Data-driven Framework for Information Extraction from Chinese Electronic Medical Records                                          
    supervised by Prof. Kenny Q. Zhu, from 2014.05 to 2015.01

• Refined a data-driven, mostly unsupervised algorithm for information extraction.
• Implemented a conditional random field (CRF) algorithm for name entity recognition and demonstrated our data-driven framework outperformed CRF by 5%.
  
• Implemented a support vector machine (SVM) algorithm for auxiliary description matching and demonstrated our data-driven framework outperformed SVM by 16%.

 

    Parallelizationt for A Machine Learning Application by Boost
     supervised by Prof. Geoffrey I. Webb and Ana M. Martinez, PhD, from 2013.09 to 2013.11

• Skilful at basic usage of Boost.
• Accelerated Petal, a machine learning application for classification learning from very large data.