Students in the School of Data Science and Artificial Intelligence can choose from three major tracks: Artificial Intelligence, Data Science, and Computational Applied Mathematics. All of these tracks strengthen the critical thinking and creative thinking skills they developed in the School of Innovation Foundations.

All major courses are based on computational competence, so at least one computer language must be studied in advance.


Classes are conducted in a student-led active learning format, and students perform individual or group hands-on projects to acquire practical competencies as well as theory and methodology. Instructors often act as mentors and facilitators to help students acquire core skills and knowledge at each level and solve problems on their own.


Students can not only acquire comprehensive theoretical knowledge of data science and artificial intelligence and practical skills in computing technology, but also enhance their major competencies by choosing courses from other undergraduate majors. In this way, students acquire creative, convergent, and complex thinking skills to discover and solve various complex problems in society and industry.


All classes focus on group-based active learning, including focused discussions and project activities, and various remote collaboration tools. In addition, the TAEJAE metaverse campus is provided to facilitate team collaboration.


Students gain insight and experience as global leaders from traveling and learning in five different countries around the globe. In each city, students carry out a Global Engagement project, which connects classwork with the local society and industry.


After choosing a major track, students select an advisor and conduct Capstone Seminars over a period of three semesters, from junior to the first semester of senior.


The Capstone Seminars culminate in a Capstone Project. Students complete this project during the 2nd semester of senior.


Senior researchers, CEOs, and executives from corporations with experience solving real-world problems will act as adjunct faculty to support the design and implementation of Capstone Projects, in part by establishing collaborations with actual businesses.


The three major tracks in the School of Data Science and Artificial Intelligence are: Artificial Intelligence (AI), Data Science (DS), and Computational Applied Mathematics (CM).

The Artificial Intelligence (AI) track focuses on a wide variety of artificial intelligence knowledge and skills. It requires learning not only methods and theory, but also software technology. Students in this track also learn to identify and creatively solve problems, drawing on skills and knowledge from social, economic, industrial, and cultural fields. Because artificial intelligence uses computer science and engineering techniques to solve human and social problems, students in this track should have interdisciplinary interests that lead them to want to advance society. The courses, majors, electives, and Capstone Projects are designed to help students become leaders and innovations in an increasingly technological world.


The Data Science (DS) track focuses on identifying and solving problems in social, economic, industrial, and cultural fields from the data perspective. The subject matter in this track is based on mathematics, probability, statistics, and computer science. Students acquire a variety of data handling skills as well as analytical thinking skills so that they can abstract and solve problems from a data science perspective.


The Computational Applied Mathematics (CM) track focuses on applying and solving real-world problems and mathematical problems by using machine learning, deep learning, and data science. This track leads students to apply computation based on traditional mathematical knowledge and thinking skills. It also leads students to have new mathematical insights, which can be used to devise new types of data structures, algorithms, and rigorous approaches to complex problems. Such problems range from highly theoretical to highly practical.

Data Science and Artificial Intelligence

Jiyoung Kwahk

Ph.D. / Pohang University of Science and Technology (POSTECH) /

Industrial Engineering (Human-Computer Interaction)
E-mail :

Jiyoung Kwahk is an EXECUTIVE-LEVEL USER EXPERIENCE RESEARCH AND DESIGN SPECIALIST with a Human Factors background specialized in theories, principles, methodologies, technologies, and practices of multiple disciplines around Human-Computer Interaction. Dean Kwahk is experienced in leading design and development teams in various application domains, such as smart consumer electronics, connected devices, and software applications for consumer and enterprise uses. Her recent research interests include applying a service design approach to complex human-machine systems like smart homes, smart enterprises, or smart cities, wherein people, devices, and services are interconnected, which cause complicated user experience problems. She excels in capturing meaningful user needs and converting them into new products and business opportunities, and she is eager to share her expertise and collaborate with others to accomplish the shared vision and goals of TAEJAE. Dean Kwahk is a warm-hearted design thinker with empathy and great enthusiasm who wishes to help students contribute to making the world a better place.

•  DEPUTY DIRECTOR, Future City Open Innovation Center (FOIC) (Feb 2016 – Feb 2023)
•  Industry-Academia Collaboration Professor, Department of Industrial and Management Engineering, POSTECH (Feb 2016 – Feb 2023)
•  Adjunct Professor, Yonsei University (Feb 2016 – Feb 2023)
•  Vice President, UX Design Group, Corporate Design Center, Samsung Electronics Co., Ltd. (Sep 2002 – Dec 2015)
•  Principal Designer, UX Convergence Group, DMC R&D Center, Samsung Electronics Co., Ltd. (Sep 2002 – Dec 2015)
•  Senior Researcher, UI Lab, DMC R&D Center, Samsung Electronics Co., Ltd. (Sep 2002 – Dec 2015)
•  Senior Research Associate, Human-Computer Interaction Lab., Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech) (Aug 2000 – Aug 2002)
•  Postdoctoral Researcher, Human-Computer Interaction Lab., Dept. of Industrial Engineering, POSTECH (Aug 1999 – Aug 2000)

•  Certificate of Commendation by Presidential Commission on Balanced National Development (Sep 2020)
•  Certificate of Commendation by Kyeongbuk Province (Sep 2020)

•  H. Jang, H. Ryu, and J. Kwahk*, (2023), “A framework for simulating the suitability of data usage in designing smart city services“, Journal of Urban Planning and Development.

•  J. Kwahk, (2023), “Chapter 12. Towards a Livable and Lovable Smart City: Collaborative Journey Into the Human-centered Future”, <What We Need for Future City>.

•  K. Park, S. H. Han, H. Lee, J. Kwahk, (2021), “Shared steering control: How strong and how prompt should the intervention be for a better driving experience?”, International Journal of Industrial Ergonomics 86, 103213.

•  K. Park, J. Kwahk, and S. H. Han, (2021), “Toward Trustworthy and Comfortable Lane Keeping Assistance System: An Empirical Study of the Level of Haptic Authority”, International Journal of Human–Computer Interaction, 1-17.

•  M. Lee, J. Kwahk*, S. H. Han, D. Jeong, K. Park, S. Oh, and G. Chae, (2020), “Developing personas & use cases with user survey data: A study on the millennials’ media usage”, Journal of Retailing and Consumer Services, 54, 102051.

•  M. Lee, J. Kwahk*, S.H. Han, H. Lee, (2020), “Relative Pointing Interface: A gesture interaction method based on the ability to divide space”, International Journal of Industrial Ergonomics, 75, 102878.

•  K. Park, J. Kwahk*, S. H. Han, M. Song, D. G. Choi, H. Jang, D. Kim, Y. D. Won, and I. S. Jeong, (2019), “Modelling the Intrusive Feelings of Advanced Driver Assistance Systems based on Vehicle Activity Log Data”, International Journal of Automotive Technology, 20(3), 455-463.

•  H. Moon, S. H. Han, and J. Kwahk*, (2019), “ A MORF-Vision method for strategic creation of IoT solution opportunities”, International Journal of Human-Computer Interaction, 35(10), 821-830.

•  D. Y. Jeong, J. Kwahk*, S. H. Han, J. Park, M. Lee, and H. Jang, (2018), “A Pedestrian Experience Framework to Help Identify Impediments to Walking by Mobility-Challenged Pedestrians”, Journal of Transport & Health, 10, 334-349.

•  J. Park, S. H. Han, J. Park, J. Park, J. Kwahk, M. Lee, D. Y. Jeong, (2018), “Development of a Web-based User Experience Evaluation System for Home Appliances”, International Journal of Industrial Ergonomics, 67, 216-228.

•  J. Kwahk and S. H. Han, (2002), “A methodology for evaluating the usability of audiovisual consumer electronic products”, Applied Ergonomics, 33(5), 419-431.

•  K-J. Kim, S. H. Han, M. H. Yun, and J. Kwahk, (2002), “A systematic procedure for modeling usability based on product design variables: a case study in audiovisual consumer electronic products”, International Journal of Occupational Safety and Ergonomics, 8(3), 387-406.

•  T. Smith-Jackson, J. Kwahk, R. Williges, (2002), “Senior healthwatch: Designing a health monitoring and information interface for a smart house”, Gerontechnology, 2(1), 119.

•  S. H. Han, M. H., Yun, J. Kwahk, and S. W. Hong, (2001), “Usability of consumer electronic products”, International Journal of Industrial Ergonomics, 28(3), 143-151.

•  S. H. Han and J. Kwahk, (2000), “Designing a single line display menu for the user interface of consumer electronic products”, Asian Journal of Ergonomics, 1(1), 25-42.

•  S. H. Han, M. H. Yun, K. Kim, and J. Kwahk, (2000), “Evaluation of product usability: Development and validation of usability dimensions and design elements based on empirical models”, International Journal of Industrial Ergonomics, 26(4), 477-488.

•  M. H. Yun, S. H. Han, S. W. Hong, J. Kwahk, and Y. H. Lee, (2000), “Development of a systematic checklist for the human factors evaluation of the operator aiding system in a nuclear power plant”, International Journal of Industrial Ergonomics, 25(6), 597-609.

•  S. H. Han, B. Kim, J. Kwahk, S. W. Hong, and H. J. Eoh, (2000), “Development of a user friendly safety information system based on an accident database”, Engineering Design and Automation, 111.

•  S. H. Han, M. Song, and J. Kwahk, (1999), “A systematic method for analyzing magnitude estimation data”, International Journal of Industrial Ergonomics, 23(5-6), 513-524.

•  S. H. Han, E. S. Jung, M. Jung, J. Kwahk, and S. Park, (1998), “Psychophysical methods and passenger preferences of interior designs”, Applied Ergonomics, 29(6), 499-506.

•  S. H. Han, J. Kwahk, and M. K. Chung, (1997), “Displays and controls on a VDT-based operator interface of a control room”, The Japanese Journal of Ergonomics, 33, 64-65.

•  S. H. Han, S. Cho, J. Kwahk, M. Kim, and K. Cho, (1996), “Design guidelines on character rejection and substitution for off-line document processing systems”, International Journal of Industrial Ergonomics, 18(2-3), 161-171.

•  S. H. Han, E. S. Jung, M. Jung, J. Kwahk, S. Park, and J. Choe, (1994), “A psychophysical evaluation of interior design alternatives for a high speed train”, Computers and Industrial Engineering, 27, 397-400.

Joo-Haeng Lee

Ph.D. / Pohang University of Science and Technology (POSTECH) /

Computer Science and Engineering
E-mail :

•  Co-Founder / CEO, Pebblous Inc (2021-Present)
•  Professor, Department of Computer Software and Engineering, University of Science and Technology (2008-Present)
•  Principal Research Scientist, Electronics and Telecommunications Research Institute (1999-Present)
•  Visiting Professor, Graduate School of Information Science and Engineering, Ritsumeikan University (2012-2013)

•  Wolfram Innovator Award, Wolfram Research (2019)
•  Kahun Award, Kahun Academy Foundation and Korea Society of CAD/CAM Engineers (2015 & 2005)
•  Presentation Paper Award, The 11th Korea Robotics Society Annual Conference (2015)
•  Best Paper Award (2nd Place), Korea Society of CAD/CAM Engineers (2009)
•  Best Paper Award, 2008 Annual Conference of Korea Society of CAD/CAM Engineers (2008)
•  Best Paper Award, 2005 Korea-China Joint Conference on Geometric and Visual (2005)
•  Nominee of Best Paper Award, Korea Society of CAD/CAM Engineers (2005 & 2004)

•  Taewoo Kim, and Joo-Haeng Lee, (2020), “Reinforcement learning-based path generation using sequential pattern reduction and self-directed curriculum learning”, IEEE Access, 8, 147790-147807.

•  Ahun Lee, Joo-Haeng Lee, Jaehong Kim, (2016), “Data-Driven Kinematic Control for Robotic Spatial Augmented Reality System with Loose Kinematic Specification”, ETRI Journal, 38(2), 337-346.