- 12월 첫수융합포럼 안내 The First Wednesday Multidisciplinary Forum, December, 2012
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- 2012-12-04 14:35:09|
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12월 첫수융합포럼 안내 The First Wednesday Multidisciplinary Forum, December, 2012
첫수 융합포럼 개최 안내
The First Wednesday Multidisciplinary Forum
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문화기술대학원, 산업 및 시스템 공학과, 지식서비스공학과 등 3개 학과가 공동으로 주관하는
12월 첫수 융합포럼에 여러분을 초대합니다.
Graduate School of Culture Technology, Department of Industrial & Systems Engineering,
and Department of Knowledge Service Engineering would like to invite KAIST students to
"The First Wednesday Multidisciplinary Forum” on December 5(Wed), 2012.
❏ 일시 : 2012. 12. 5(수) 정오12:00
Date : December 5(Wed), 2012, 12:00 at noon
❏ 장소 : 창의학습관(Bldg# E11) 101호
Venue : Room# 101, Creative Learning Bldg. (#E11)
❏ 연사 및 발표 주제
학과명 Dept.
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연사 Speaker
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발표 주제 Topic to present
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문화기술대학원
Graduate School of Culture Technology, KAIST
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박주용 교수
Prof. Park, juyong
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① Inference of rankings from data: Bayesian-approach to determining dominance hierarchy
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산업 및 시스템 공학과
Department of Industrial & Systems Engineering, KAIST
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문일철 교수
Prof. Moon, Il Chul
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② Predicting the Stock Market with Unstructured Media Texts
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지식서비스공학과
Department of Knowledge Service Engineering, KAIST
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권가진 교수
Prof. Gweon, Gahgene
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③ Cracking the code in social interactions
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❏ Abstract
① Inference of rankings from data: Bayesian-approach to determining dominance
hierarchy
Bayes' theorem, first formulated by the British mathematician Thomas Bayes in the 18th
century, still functions as the standard methodology by which one's distribution of a
parameter is updated according to the unearthing of new empirical evidence. Here we
apply this method to the problem of ranking nodes in a competition network, which
produces a confidence interval in the rankings, unlike centrality-based methods.
② Predicting the Stock Market with Unstructured Media Texts
I present the result of predicting the stock market index with unstructured media texts.
The stock market index is a single measurement of a collective behavior of investors,
and from such perspective, I conjecture that the collective would be better explained
by utilizing the media data generated by the individuals in the collective. I compare
and contrast the prediction accuracy and the values of the traditional economic
indicators as well as the recent unstructured text-mining data.
③ Cracking the code in social interactions
My general research goal is in automatically analyzing various social interactions in
order to gain understanding on social phenomena and using that understanding to
improve quality of life (e.g. education, relationships). Analyzing and making sense of
social interactions, more specifically conversations, is analogous to cracking the code
that’s hidden under seemingly ordinary conversations. In this talk, I highlight the
necessity of such research along with some example research work in this area.
❏ 간단한 점심식사를 준비할 예정입니다.
Refreshments will be served for free.
❏ 관련문의 /Inquiry :
문화기술대학원 이윤정 / Ext. 2902, yclee@kaist.ac.kr (Mrs. Lee, Y. J.)
산업 및 시스템 공학과 노수정 / Ext. 3102, nohsj@kaist.ac.kr (Mrs. Noh, S. J.)
지식서비스공학과 최진희 / Ext. 1603, choijh84@kaist.ac.kr (Mrs. Lee, H. M.)
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