Title: Lessons Learned from Running Interdisciplinary Computational Sciences Program for Undergraduate at Seoul National University on the Future of Scientific Computing Education

Abstract:

자연과학은 자연을 대상으로 한 관측이 주된 목표이고 우리 자연과학자들은 관측자료 즉 데이터를 통해 자연(모델)을 이해하는 것에 매우 익숙하다. 그런데 최근 갑자기 데이터과학, 인공지능이며 머신러닝이 4차 산업혁명이라는 buzzword와 함께 큰 화제이다. 기업(enterprise) 차원에서는 이것들이 새로운 분야이자 기회인 것 처럼 보일지 모르지만 아직 자연과학 분야에서의 기여도는 제한적이다. 정말 인공지능 머신러닝을 통해 새로운 자연현상이 밝혀진 사례는 최소한 내가 속한 지질해양분야에서는 그리 많은 것 같지 않다. 그럼에도 불구하고 학부생들과 대학원생들에게 새로운 분야를 가르쳐야하는 교육자로써 지난 5-6 년간 교육현장에서 느낀 점들을 공유하고자 한다. 특히 enterprise AI와 달리 scientific AI가 나아가야 할 방향 그리고 컴퓨터과학의 발달로 인해 달라진 ICT의 생태계 속에서 어떻게 미래문제해결형 학생을 양성하기 위한 교육이 이루어져야 하는가에 대해 의견을 제시하고자 한다. 이는 현재 서울대 계산과학 연합전공과 협동과정이 추구하는 방향이기도 하다.

Science is all about observation and measurement. So for natural scientists like us we should be very comfortable about how to derive our understanding into models from observed 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 buzzword ‘The Fourth Industrial Revolution’ as well as terms such as data science, machine learning and artificial intelligence. However, at least in my field of marine and Earth sciences, the impact brought about by these new disciplines and approach have been somewhat limited because in science we never get enough data as we want. These new approaches require immense 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 would 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 raise competent next-generation scientists to deal with future problems. I hope I can suggest a better picture of scientific computation in this world of AI which we had computational sciences program of Seoul National University is also trying to find.

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