Difference between revisions of "Daesik Kim"

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| <font size = 5> '''[[Dae-sik Kim]] (김대식)'''</font><br><br>
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| <font size = 5> '''[[Daesik Kim]] (김대식)'''</font><br><br>
:서울대학교 융합과학부 박사과정 ( ~ ) <br>
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:서울대학교 융합과학부 석박사통합과정 졸업 (2014.09 ~ 2019.08) <br>
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:포항공과대학교 산업경영공학과 졸업 (2007.08) <br>
 
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:Tel: +82-31-888-9579 <br>
 
:Tel: +82-31-888-9579 <br>
:e-mail:  <br>
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:e-mail: daesik.kim@snu.ac.kr <br>
 
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== '''Research Interests''' ==
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* Machine Learning, Multi-modal Deep Learning, Knowledge based Deep learning
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== '''Education''' ==
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* B.Sc. Industrial Engineering and Management, Pohang University of Science and Technology, 2007
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== '''Employment''' ==
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* System developer, Programmer(Powerbuilder, Oracle), Mega IT. 2002-2005
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* Assistant Manager ,Tresury department, Kookmin bank, 2007-2014
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* Founder & CEO/CTO in '''[http://www.vdotdo.com V.DO]''', 2017~
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== '''Publication''' ==
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https://scholar.google.com/citations?user=YUcWWbEAAAAJ&hl=en
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* Seo, J., Kim, D., Suh, B., & Lee, J. (2015, April). "[https://dl.acm.org/citation.cfm?id=2732858&dl=ACM&coll=DL Design of a Smart TV Logging System Using Beacons and Smartphones]", In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2157-2162). ACM.
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* Seonhoon Kim, Daesik Kim and Bongwon Suh "[https://dl.acm.org/citation.cfm?id=2903686&dl=ACM&coll=DL Music Genre Classification using Multimodal Deep Learning]"  Proceedings of HCI Korea '16
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* Daesik Kim, Myunggi Lee and Nojun Kwak, "[http://ieeexplore.ieee.org/document/7965886/ Matching Video Net: Memory-based embedding for video action recognition]", International Joint Conference on Neural Networks 2017 (IJCNN2017), Anchorage, AK, May 2017.
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* Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang and Nojun Kwak, "[https://arxiv.org/pdf/1707.00251.pdf Where to Play: Retrieval of video segments using natural-language queries]", arXiv, July 2017.
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* Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee and Nojun Kwak, "[http://openaccess.thecvf.com/content_cvpr_2018/papers/Kim_Dynamic_Graph_Generation_CVPR_2018_paper.pdf Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams]", The Thirtieth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2018), accepted.
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* Daesik Kim, Seonhoon Kim, and Nojun Kwak, "Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension", 57th Annual Meeting of the Association for Computational Linguistics (ACL2019), Florence, Italy. July 2019 (accepted, [https://arxiv.org/abs/1811.00232 arXiv]).
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== '''Competition''' ==
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* 4th place of "Textbook Question Answering Challenge", CVPR 2017 Workshop on Visual Understanding Across Modalities
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== '''Teaching''' ==
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* Tensorflow tutorial at OSIA (Aug. 2016)
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* Tensorflow tutorial at CONTECH (Apr. 2017)
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* Deeplearning class for artists at NABI artcenter (oct.~ dec. 2017, 8 weeks)

Latest revision as of 14:45, 21 October 2019

Kds.jpg Daesik Kim (김대식)

서울대학교 융합과학부 석박사통합과정 졸업 (2014.09 ~ 2019.08)
포항공과대학교 산업경영공학과 졸업 (2007.08)


Tel: +82-31-888-9579
e-mail: daesik.kim@snu.ac.kr


Research Interests

  • Machine Learning, Multi-modal Deep Learning, Knowledge based Deep learning


Education

  • B.Sc. Industrial Engineering and Management, Pohang University of Science and Technology, 2007


Employment

  • System developer, Programmer(Powerbuilder, Oracle), Mega IT. 2002-2005
  • Assistant Manager ,Tresury department, Kookmin bank, 2007-2014
  • Founder & CEO/CTO in V.DO, 2017~

Publication

https://scholar.google.com/citations?user=YUcWWbEAAAAJ&hl=en

  • Daesik Kim, Seonhoon Kim, and Nojun Kwak, "Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension", 57th Annual Meeting of the Association for Computational Linguistics (ACL2019), Florence, Italy. July 2019 (accepted, arXiv).

Competition

  • 4th place of "Textbook Question Answering Challenge", CVPR 2017 Workshop on Visual Understanding Across Modalities


Teaching

  • Tensorflow tutorial at OSIA (Aug. 2016)
  • Tensorflow tutorial at CONTECH (Apr. 2017)
  • Deeplearning class for artists at NABI artcenter (oct.~ dec. 2017, 8 weeks)