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BEGIN:VEVENT
SUMMARY:AI Applications for Satellite-based Disaster Monitoring and Predic
 tion
DTSTART;VALUE=DATE-TIME:20191107T023000Z
DTEND;VALUE=DATE-TIME:20191107T025000Z
DTSTAMP;VALUE=DATE-TIME:20260724T171405Z
UID:indico-contribution-11-29@sshep.snu.ac.kr
DESCRIPTION:Speakers: Jungho Im (UNIST)\nhttps://sshep.snu.ac.kr/event/107
 /contributions/29/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Deep learning for multi-year ENSO forecasts
DTSTART;VALUE=DATE-TIME:20191107T021000Z
DTEND;VALUE=DATE-TIME:20191107T023000Z
DTSTAMP;VALUE=DATE-TIME:20260724T171405Z
UID:indico-contribution-11-21@sshep.snu.ac.kr
DESCRIPTION:Speakers: Yoo-Geun Ham (Chonnam National University)\nVariatio
 ns in the El Niño/Southern Oscillation (ENSO) are associated with a wide 
 array of regional climate extremes and ecosystem impacts. Robust\, long-le
 ad forecasts would therefore be valuable for managing policy responses. Bu
 t despite decades of effort\, forecasting ENSO events at lead times of mor
 e than one year remains problematic. Here we show that a statistical forec
 ast model employing a deep-learning approach produces skilful ENSO forecas
 ts for lead times of up to one and a half years. To circumvent the limited
  amount of observation data\, we use transfer learning to train a convolut
 ional neural network (CNN) first on historical simulations and subsequentl
 y on reanalysis from 1871 to 1973. During the validation period from 1984 
 to 2017\, the all-season correlation skill of the Nino3.4 index of the CNN
  model is much higher than those of current state-of-the-art dynamical for
 ecast systems. The CNN model is also better at predicting the detailed zon
 al distribution of sea surface temperatures\, overcoming a weakness of dyn
 amical forecast models. A heat map analysis indicates that the CNN model p
 redicts ENSO events using physically reasonable precursors. The CNN model 
 is thus a powerful tool for both the prediction of ENSO events and for the
  analysis of their associated complex mechanisms.\n\n\nReferences:  \n\n[h
 ttps://www.nature.com/articles/s41586-019-1559-7][1]\n\n[https://www.scien
 cemag.org/news/2019/09/artificial-intelligence-could-predict-el-ni-o-18-mo
 nths-advance][2]\n\n[https://www.yna.co.kr/view/AKR20190918093900017?input
 =1195m][3]\n\n[http://biz.chosun.com/site/data/html_dir/2019/09/19/2019091
 900659.html][4]\n\n\n  [1]: https://www.nature.com/articles/s41586-019-155
 9-7\n  [2]: https://www.sciencemag.org/news/2019/09/artificial-intelligenc
 e-could-predict-el-ni-o-18-months-advance\n  [3]: https://www.yna.co.kr/vi
 ew/AKR20190918093900017?input=1195m\n  [4]: http://biz.chosun.com/site/dat
 a/html_dir/2019/09/19/2019091900659.html\n\nhttps://sshep.snu.ac.kr/event/
 107/contributions/21/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Deep Learning for Sea Ice Classification and Ship Detection using 
 Satellite Synthetic Aperture Radar
DTSTART;VALUE=DATE-TIME:20191107T015000Z
DTEND;VALUE=DATE-TIME:20191107T021000Z
DTSTAMP;VALUE=DATE-TIME:20260724T171405Z
UID:indico-contribution-11-26@sshep.snu.ac.kr
DESCRIPTION:Speakers: Duk-jin Kim (Seoul National University)\nhttps://ssh
 ep.snu.ac.kr/event/107/contributions/26/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lessons Learned from Running Interdisciplinary Computational Scien
 ces Program for Undergraduate at Seoul National University on the Future o
 f Scientific Computing Education
DTSTART;VALUE=DATE-TIME:20191107T012000Z
DTEND;VALUE=DATE-TIME:20191107T015000Z
DTSTAMP;VALUE=DATE-TIME:20260724T171405Z
UID:indico-contribution-11-27@sshep.snu.ac.kr
DESCRIPTION:Speakers: Sang-Mook Lee (Seoul National University)\n자연과
 학은 자연을 대상으로 한 관측이 주된 목표이고 우리 자
 연과학자들은 관측자료 즉 데이터를 통해 자연(모델)을 
 이해하는 것에 매우 익숙하다. 그런데 최근 갑자기 데이
 터과학\, 인공지능이며 머신러닝이 4차 산업혁명이라는 
 buzzword와 함께 큰 화제이다. 기업(enterprise) 차원에서는 
 이것들이 새로운 분야이자 기회인 것 처럼 보일지 모르
 지만 아직 자연과학 분야에서의 기여도는 제한적이다. 
 정말 인공지능 머신러닝을 통해 새로운 자연현상이 밝
 혀진 사례는 최소한 내가 속한 지질해양분야에서는 그
 리 많은 것 같지 않다. 그럼에도 불구하고 학부생들과 
 대학원생들에게 새로운 분야를 가르쳐야하는 교육자로
 써 지난 5-6 년간 교육현장에서 느낀 점들을 공유하고자 
 한다. 특히 enterprise AI와 달리 scientific AI가 나아가야 할 
 방향 그리고 컴퓨터과학의 발달로 인해 달라진 ICT의 생
 태계 속에서 어떻게 미래문제해결형 학생을 양성하기 
 위한 교육이 이루어져야 하는가에 대해 의견을 제시하
 고자 한다. 이는 현재 서울대 계산과학 연합전공과 협동
 과정이 추구하는 방향이기도 하다. \n\nScience is all about ob
 servation and measurement. So for natural scientists like us we should be 
 very comfortable about how to derive our understanding into models from ob
 served data sets. This should be very familiar to us and It is the crux of
  what we do day to day. Recently\, however\, we are puzzled by the new buz
 zword ‘The Fourth Industrial Revolution’ as well as terms such as data
  science\, machine learning and artificial intelligence. However\, at leas
 t in my field of marine and Earth sciences\, the impact brought about by t
 hese new disciplines and approach have been somewhat limited because in sc
 ience we never get enough data as we want. These new approaches require im
 mense amounts of data\, which sometimes is not practical. As a person who 
 has been involved as an educator in the midst of these new changes\, I wou
 ld like to share my views on how we can the important differences between 
 enterprise AI and scientific approaches. It is important to teach students
  probably under these new changes because our goal has always been to rais
 e competent next-generation scientists to deal with future problems. I hop
 e I can suggest a better picture of scientific computation in this world o
 f AI which we had computational sciences program of Seoul National Univers
 ity is also trying to find.\n\nhttps://sshep.snu.ac.kr/event/107/contribut
 ions/27/
LOCATION:Seoul National University\, College of Natural Sciences\, Buildin
 g 25-1 국제회의실 (International Conference Hall)
URL:https://sshep.snu.ac.kr/event/107/contributions/27/
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