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2018, Fall / 2019, Spring Admission Information Session for Graduate School of Knowledge Service Engineering

 In early 2016, the World Economic Forum, which was held in Davos, Switzerland, introduced the term, the Fourth Industrial Revolution for the first time. Based on developments in information technology that is the cornerstone of the Third Industrial Revolution, the Fourth Industrial Revolution aims to create newly added values by integrating a variety of technologies and industries.

The key points of the Fourth Industrial Revolution are: (1) the hyper-connectivity between human beings and things, (2) the hyper-intelligence derived from pattern analysis of a huge amount of data, and (3) creation of the new values by understanding and predicting human behavior. The market demands also reflect technological trends of the Fourth Industrial Revolution, as the 2017’s Consumer Electronics Show mainly focused on integration of Internet of Things (IoT) and artificial intelligence (AI).

To play a leading role in the Fourth Industrial Revolution, it is required to deeply understand to process and analyze a huge amount of data as well as human itself. To this end, Graduate School of Knowledge Service Engineering (KSE) has studied on Knowledge Systems, Data Science, and Human-Computer Interaction (HCI), and has cultivate competent researchers on such areas.

In the field of Knowledge Systems, the Graduate School of KSE has conducted researches on supporting optimal decision making, including development of a self-evolving experiential knowledge platform, supports decision making of fields experts by exploiting experiential knowledge obtained through self-learning process, and development of a predictive model of the current value of real estate properties using big data analytics.

In the field of Data Science, several research projects have been conducted to extract meaningful information/knowledge from a huge amount of data and to design a variety of practical services: smart cloudlet technology to support mobile big data processing, trajectory social pattern discovery to process big data with a distributed processing algorithm, and theoretical approach to resolve deep learning problems in distributed environment.

In the field of HCI, issues on user experience and interface design have been treated, including context-aware intervention for smart-device interruption to support a user to handle many interruptions and distractions from a variety of smart devices, and experiential knowledge interaction technology  for knowledge convergence to minimize a user’s cognitive load.

Since the Graduate School of Knowledge Service Engineering was established in 2009, we have made a plenty of research results on the areas that closely related to the core technologies of the Fourth Industrial Revolution, such as big data analytics, IoT, and AI

The Graduate School of Knowledge Service Engineering is recruiting prospective members to play a leading role in rapidly changing world. We are holding an admission information session on April 2nd, where further information on the school and research areas will be elaborated. In addition, masters and Ph.D. students are planning to attend the session to inform of questions about research topics and school life. We welcome you to attend the session and enjoy refreshments.

For further information about the school, please check the school’s official website (http://kse.kaist.ac.kr) or feel free to contact any students below.  We await your participation.

Thank you.

# Session Schedule

+ Daejeon: April 2nd 12:00 pm / Room 101, Creative Learning Center (E11; 창의관), KAIST main campus (Map: http://bit.ly/1LuKqYc)

+ Seoul: April 2nd 07:00 pm / Toz Shinchon Business Center (Map : http://bit.ly/P8Po0N)

# Early-bird Registration: http://bit.ly/2FJd0ny

+ 1st Half: Lecture on Knowledge Service

+ 2nd Half : Admission information session

※ A meal will be provided if you registered

# Websites

+ Official website: http://kse.kaist.ac.kr

+ Facebook: https://www.facebook.com/ksekaist/

+ Twitter: https://twitter.com/kaist_kse

+ Youtube: http://bit.ly/2pdnMeq

# Additional Materials

+ Examples of entrance exam : http://bit.ly/2HzFv7D

+ Introductory videos (2016): http://bit.ly/2Gsmgh2

+ Recording of previous info. session (2017 Spring): https://youtu.be/bO2au2tQlhI

+ Recording of previous info. session (2017 Fall): https://youtu.be/rum4tjQrf9s

# Contact
+ SiMin Sung, M.S. Student (Advisor: Prof. Wan Chul Yoon) -  sa7377@kaist.ac.kr
+ Seungwoo Choi, Ph.D Student (Advisor: Mun Yong Yi) - sw.choi@kaist.ac.kr
+ Younghyun Hong, Ph.D Student (Advisor: Prof. Mun Yong Yi) - yh.hong@kaist.ac.kr
+ Woohyeok Choi, Ph.D Student (Advisor: Prof. Uichin Lee) - woohyeok.choi@kaist.ac.kr
+ Jongwon Kim, M.S. Student (Advisor: Prof. Uichin Lee) - jong1k@kaist.ac.kr
+ Minsoo Choy, Ph.D Student (Advisor: Prof. Jae-Gil Lee) - minsoo.choy@kaist.ac.kr
+ Minseok Kim, M.S. Student (Advisor: Prof. Jae-Gil Lee) - minseokkim@kaist.ac.kr
+ Sangmook Kim, M.S. Student (Advisor: Prof. Se-Young Yun) - sangmook.kim@kaist.ac.kr
+ Junghoon Kim, M.S. Student(Advisor: Prof. Se-Young Yun) - junghunkim@kaist.ac.kr




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