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In Cheol
Kim
,
PhD
Senior System Analyst
Location: 
38A
/
10S1021C
Phone Number: (
301
827-4757
Expertise and Research Interests: 

Dr. In Cheol Kim has been working at the Lister Hill National Center for Biomedical Communications (LHNCBC), U.S. National Library of Medicine, Bethesda, Maryland since 2004. He has a PhD in Information Processing Engineering from the Kyungpook National University, South Korea (2001). His previous experience includes two years as a postdoctoral researcher at the Concordia University, Montreal, Canada. In addition, he worked as a senior system engineer for more than five years in an industrial research lab. Dr. Kim’s research interests are Web-based document analysis and processing, pattern recognition and classification, machine learning, and multi-modal human computer interaction.

Honors and Awards: 

Dr. In Cheol  Kim received NLM’s Special Act or Service Group Award in 2012.

Publications/Tools by In Cheol Kim: 
Kim I, Thoma GR. Automated Classification of Author’s Sentiments in Citation Using Machine Learning Techniques: A Preliminary Study. Proc. the 2015 IEEE Conf. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2015), Niagara Falls, Canada, Aug. 12-15, 2015.
Kim I, Le DX, Thoma GR. Automated method for extracting "citation sentences" from online biomedical articles using SVM-based text summarization technique. Proc. the 2014 IEEE Int'l Conf. on Systems, Man, and Cybernetics (SMC 2014), pp. 2006-2011, San Diego, October, 2014
Kim I, Le DX, Thoma GR. Identifying “comment-on” citation data in online biomedical articles using SVM-based text summarization technique. Proc. Int’l Conf. Artificial Intelligence (ICAI’12), vol. 1, pp. 431-437, Las Vegas, July 2012.
Kim I, Le DX, Thoma GR. Automated identification of biomedical article type using support vector machines. Proc. 18th SPIE Document Recognition and Retrieval, 7874:787403 (1-9), San Francisco, January 2011.
Thoma GR, Le DX, Kim I, Kim JW, Moon C, Tran L, Zou J. Automation to Accelerate the Production of MEDLINE April 2008 Technical Report to the LHNCBC Board of Scientific Counselors.
Kim IC, Le DX, Thoma GR. Hybrid approach combining contextual and statistical information for identifying and statistical information for identifying MEDLINE citation terms. Proc. SPIE-IS/T Electronic Imaging. San Jose, CA. January 2008;6815:68150P(1-9)
Kim IC, Le DX, Thoma GR. Identification of "comment-on sentences" in online biomedical documents using support vector machines. Proc. SPIE conference on Document Recognition and Retrieval, 6500:65000O (1-8), San Jose, January 2007.
Kim I, Le DX, Thoma GR. Automated Cleanup Processing for Extracting Bibliographic Data from Biomedical Online Journals In: Callaos N, Lesso W, editors. SCI 2005. Proc. 9th World Multiconference on Systemics, Cybernetics and Informatics; 2005 Jul 10-13; Vol. 4; Orlando (FL): International Institute of Informatics and Systemics; c2005. 401-5
Kim KM, Park JJ, Song MH, Kim I, Suen CY. Binary decision tree using K-means and genetic algorithm for recognizing defect patterns of cold mill strip. Lecture Notes in Artificial Intelligence (LNAI), vol. 3029, pp. 341-350, May, 2004.
Kim IC, Kim KK, Suen CY. Word separation in handwritten legal amounts on bank cheques based on spatial gap distances. Lecture Notes in Artificial Intelligence (LNAI), vol. 3029, pp. 453-462, May, 2004.

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