Courses

  • Instructor: Joseph Seering
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: Designing useful and usable user interfaces is much more than designing fancy and beautiful things. This course introduces fundamental principles, techniques, and methods for designing, prototyping, and evaluating user interfaces. Through this course, you’ll master the skills to design useful and usable interfaces that are carefully catered to users’ needs.
  • Instructor: Geehyuk Lee
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: A wearable computer is a computing device worn on the body. Examples are smart glasses, smart watches, smart earphones, and smart rings. In a broader sense, body-worn devices for AR/VR applications and human-sensing devices for sport and medical purposes are also examples of a wearable computer. This course is about interaction devices and techniques for wearable computers. This course consists of three sets of lectures: User Interfaces (UI), Maker Lab (ML), and Interface Channels (IC). The UI lectures cover the wide spectrum of wearable user interface concepts and examples. The ML lectures provide knowledge and hands-on experience on basic electronics and fabrication skills for prototyping a wearable interface. The IC lectures cover background knowledge on the human sensory channels, including visual, auditory, tactile, haptic, olfactory, gustatory, muscular, and brain-computer interface (BCI) channels, which will enable students to develop an insight and vision for future wearable user interfaces. In parallel to the lectures, students will conduct term projects, where they invent a new wearable interface idea, prototype and evaluate the idea, and write a paper to report the project results.
  • Instructor: Uichin Lee
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: The goal of this course is to learn the basics of how to use sensor data for designing intelligent mobile, wearable, and IoT services. The course covers the entire process of IoT data science, including data collection, pre-processing, feature extraction, machine learning modeling, and model deployment. Mobile, wearable, and IoT sensors will be mainly used, and the types of sensor data covered include motion (e.g., vibration/acceleration, GPS), physiological signals (e.g., heart rate, skin temperature), and interaction data (e.g., app usage). Students will learn the basics of digital signal processing and feature extraction techniques. Basic machine learning techniques (e.g., clustering, supervised learning, time-series learning, and deep learning) will be reviewed, and students will master these techniques with in-class practices on the Google Colab and Arduino Sense platforms. A final mini-project will help students apply the techniques learned in the class to solve real-world IoT data science problems.
  • Instructor: Uichin Lee
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: This course overviews theories and design practices in HCI fields for those graduate students who are interested in conducting HCI research. Students will learn the basic principles of HCI such as needfinding, design, and usability engineering, by actively engaging in in-class activities. Several well-known models and theories of HCI such as perception and cognition will be discussed. Furthermore, major HCI research areas such as input/output technologies, ubicomp, social computing, design process/tools, AI, and privacy & security will be reviewed by reading the key papers in those domains. Students will engage in a semester-long research project, and this will help them to improve their HCI research skills. Basic research skills (e.g., experiment design, statistical analysis, qualitative research) will be covered in the class.
  • Instructor: Sangsu Lee
  • Format: 3:1:3 (lecture: practice: credits)
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  • Instructor: Takyeon Lee
  • Format: 3:1:3 (lecture: practice: credits)
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  • Instructor: Hwajung Hong
  • Format: 3:1:3 (lecture: practice: credits)
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  • Instructor: Seok-Hyung Bae
  • Format: 3:1:3 (lecture: practice: credits)
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  • Instructor: Youn-kyung Lim
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: The objective of this course is to learn various user-centered design techniques and methods. Throughout this course, students will have opportunities to apply those methods and techniques in actual design activities and to reflect on their implications in design. The deliverables of this course will be the outcomes from a series of small design projects that require the application of the methods and the techniques students learn from this course, as well as the presentation and discussion of articles that help students establishing deeper understanding of relevant user study methods.
  • Instructor: Tek-Jin Nam
  • Format: 3:1:3 (lecture: practice: credits)
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  • Instructor: Youn-kyung Lim
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: The objective of this course is to learn the key process and the methods for qualitative data analysis in design research. This course will particularly focus on the methods that require formal qualitative data analysis so that students can experience analyzing and interpreting qualitative data to produce design knowledge. Students will have a chance to understand the types of design knowledge that can be produced by qualitative design research, and will be able to conduct two most popular qualitative design research methods requiring formal qualitative data analysis, i.e. diary study with interview and participatory workshop. For the qualitative data analysis method, students will learn the basics through understanding and applying the thematic analysis method.
  • Instructor: Sang Ho Yoon
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: The interactive experience with digital context has been evolved in the past few decades from desktop/mobile environment to virtual reality (VR). Whereas VR places a user inside a completely computer-generated environment, AR goes beyond this scope by bridging the gap between the virtual and real world seamlessly. This course will introduce theories and applications related to augmented reality technology. Students will also learn basic skills to develop augmented reality applications and content.
  • Instructor: Woontack Woo
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective:

    This course is designed to study theories, methods and applications of 3D interaction design for IoT. It provides an introduction to exciting ways of interacting with computers, with an emphasis on methods for designing and developing effective 3D user interfaces. It provides the interested students with the basic knowledge and skills to conduct the further researches in 3D Interaction through team projects. It explores:
    – Metaphors for 3D user interfaces.
    – Selecting 3D objects.
    – Manipulating 3D objects through translation, scaling, and rotation.
    – Traveling and wayfinding to get from one real or virtual place to another, and to understand where you are and where you can go.
    – System control, from menus to multimodal interaction.
    – Symbolic input, such as text. 3D interaction and tracking technology, displays, and perception.

  • Instructor: Ian Oakley
  • Format: 3:0:3 (lecture: practice: credits)
  • Objective: This lab course will allow students to learn TensorFlow Lite and deploy it on wearable and sensor-equipped microcontrollers. The course will start with a series of labs to give students basic skills and experience in this area. Building on this knowledge, students will then form teams to propose, design, and develop a novel interactive wearable computing prototype that solves a genuine user problem in an area such as authentication, accessibility, training, or mobility.