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Staff Scientist
Phone Number: (
Expertise and Research Interests: 

Jongwoo Kim received his BS degree at the Kyungpook National University, South Korea in 1989, with a major in Electronics Engineering which includes both Electrical and Computer Engineering. He then finished his MS in the same department and university in 1991. In 1998, he completed a PhD at the University of Missouri, majoring in Computer Engineering and Computer Science.

Since 1998, he has worked for several projects (MARS, WebMARS, PDRS, and IMPPOA) related to biomedical document labeling and processing areas at the Communications Engineering Branch of the Lister Hill National Center for Biomedical Communications. His research interests are biomedical document processing, biomedical image processing/recognition, pattern recognition, computer vision, fuzzy theory, and robust statistics areas.

Professional Activities: 

Dr. Kim is a member of the Institute of Electrical and Electronics Engineers (IEEE), the IEEE Computer Society, and Korean American Society of Biomedical Informatics.

Honors and Awards: 

Dr. Kim has received the NIH Group Award for his contributions to the Publisher Data Review System, and an Appreciation Award from Aquilent in 2012.

Publications/Tools by Jongwoo Kim: 
Kim J, Tran L, Chew E, Antani SK. Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning 2019 IEEE 32th International Symposium on Computer-Based Medical Systems (CBMS), pp 489-494, Cordoba, Spain, June 2019.
Kesav N, Yang Q, Losert W, Kim J, Jaeger S, Sen HN. Novel automated processing techniques of fluorescein angiography (FA) images in patients with Uveitis. Annual Meeting of the Association for Research in Vision and Ophthalmology (ARVO).
Kim J, Tran L, Chew E, Antani SK, Thoma GR. Optic Disc Segmentation in Fundus Images Using Deep Learning. SPIE Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, Vol. 10954, San Diego, USA, February 2019.
Rae A, Kim J, Le DX, Thoma GR. Main Content Detection in HTML Journal Articles. DocEng ’18: ACM Symposium on Document Engineering 2018, August 28–31, 2018, Halifax, NS, Canada. ACM, New York, NY, USA, 4 pages.
Kim J, Candemir S, Chew E, Thoma GR. Region of Interest Detection in Fundus Images Using Deep Learning and Blood Vessel Information. The 31th IEEE International Symposium on Computer-Based Medical Systems. (IEEE CBMS 2018), pp. 357-362, Karlstad, Sweden, June 2018.
Kim J, Hong S, Thoma GR. Labeling Author Affiliations in Biomedical Articles Using Markov Model Classifiers. The 13th International Conference on Data Mining (DMIN2017), pp. 105-110, Las Vegas, USA, July 2017.
Kim J, Thoma GR. Named Entity Recognition in Affiliations of Biomedical Articles Using Statistics and HMM Classifiers. The 2016 International Conference on Data Mining (DMIN2016), Las Vegas, USA, pp. 236-241, July, 2016.
Kim J, Lobuglio PS, Thoma GR. Visualization of Statistics from MEDLINE. 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS 2016), Dublin and Belfast, Ireland, pp. 290-291, June, 2016.
Kim J, Le DX, Thoma GR. Identification of Investigator Name Zones Using SVM Classifiers and Heuristic Rules. 12th international Conference on Document Analysis and Recognition (ICDAR). Washington D.C., August 2013.
Kim J, Le DX, Thoma GR. Combining SVM Classifiers to Identify Investigator Name Zones in Biomedical Articles. IS&T/SPIE’s 22nd Annual Symposium on Electronic Imaging. San Francisco, CA, January 2012; 8297.