Korea Advanced Institute of Science and Technology (KAIST)

This course provides an overview of a wide range of analysis methods for biomolecules (mostly biological macromolecules) such as proteins and DNA/RNA, and covers methods of current research of diverse fields in biochemistry 

Topics include Biomolecules, Preparation/separation (chromatography, electrophoresis), Detection (western blot, IP, ELISA, etc.), Imaging I (fluorescence, super resolution, AFM), Scattering (SAXS, DLS), Sequencing (NCS, single cell sequencing), Mass spectrometry, Structure determination (X-ray crystallography, Cryo-EM), Interaction (SPR, ITC), Single molecule techniques (FRET, magnetic tweezer. 

While there are no prerequisites for the course, coursework in Biochemistry I, Physical Chemistry I & II may be helpful. 

This advanced course covers the dynamic interactions between humans and technology. Specifically, we trace the evolution of computer-mediated communication (CMC), explore impression formation, identity, and well-being online, and extend into human–machine communication (HMC) with AI, social robots, and algorithmic media. Students critically examine theories, research, and ethical issues shaping the future of communication. Students should expect to do extensive research and produce a research paper and final paper presentation.  

Topics include Computer-mediated communication, Impression formation and relationship development, Communication and self, Psychological well-being and social support, Merging mass and interpersonal communication via interactive communication technology, Are computers social actors?   

This course covers machine learning techniques to analyze visual data. Specifically, this course focuses on fundamental machine learning and recent deep learning methods that are widely used in visual data analysis and discusses how these methods are applied to solve various problems with visual data. This course consists of lectures, practices, and projects. 

Topics include Introduction to CV/DL, Convolutional neural networks, Training, optimization, data, Few-shot learning, Object detection and segmentation, RNNS, Domain adaptation, Multimodal learning, Deployment. 

Prerequisite: Basic knowledge of Python 

This course provides an introduction to research methodology with an emphasis on experimentation. The goal of this course is to teach students how to turn an idea into a good research question and then turn that question into rigorous research studies. To do so, we survey a variety of basic and advanced research techniques, including experimental, behavioral, observational, survey, and physiological methods. Students participate in discussions to understand the applications of each class topic to their research interests. Finally, students design their own studies that utilize methodological approaches.  

Topics include Having and testing ideas, Operationalization and issues of validity, Statistical power and correlational design: measurement construction, Experimental design, Repeated sampling, Survey, Unobtrusive measures and observation, Inducing and assessing emotions, Physiological methods, Dyadic and group designs, Meta-analysis and cross-cultural research, Presenting and publishing research. 

This course introduces undergraduate students to the labs in the chemistry department. Through the lab visit experience as a small group, students learn the diverse aspects of research in cutting-edge chemistry. Groups will visit 9 labs. Students produce two term-reports and a summary regarding lab visits. 

This interdisciplinary course examines the biological, psychological, social, and cultural dimensions of sleep and circadian rhythms. We will investigate the science behind sleep: its functions, regulation, and role in health, cognition, and emotion.  

In parallel, the course will explore how sleep has been represented in literature, visual art, music, and film. We will consider how artists and thinkers have interpreted dreams, memory, insomnia, and altered states of consciousness, and how these portrayals reflect and inform our evolving understanding of the sleeping mind. 

Topics include What Is Sleep, and Why Does It Matter; The Physiology of Sleep; Circadian Rhythms and Biological Timekeeping; Sleep and the Brain; Dreams: Science and Symbolism; Sleep and Society; Sleep Disorders; Sleep in Art, Music, and Film 

There is no prerequisite for this course; however, a basic understanding of neuroscience, biology, and physiology concepts will be beneficial for students. 

This advanced course introduces the basics of artificial intelligence, which include learning, searching, knowledge management, inference, and their applications. Transformer and Large Language Model are mainly discussed in addition to other types of deep neural networks. Classical artificial intelligence topics (before the deep learning era) is also overviewed. Applications to solve web, industrial, and scientific problems with artificial intelligence will also be introduced. 

Prerequisite: It is strongly recommended that students complete other basic machine learning and deep learning courses before enrolling in this course. The instructor reviews the basics of machine learning and deep learning, but it is not a guarantee that the review will be enough for students who did not previously take any related courses.  

This course covers the fundamentals of technological innovation management and strategy. Students explore how firms and nations develop, implement, and manage technological innovation to create competitive advantages in the global market.  

The course focuses on key aspects of firms’ innovation strategy and management, including sources and types of innovation, firms’ decisions regarding market entry with new technologies, evaluation and selection of R&D projects, standards competition, and intellectual property strategies (including patenting strategy). Additionally, students develop analytical skills to work with archival data commonly used in quantitative technology management research, including metadata from research papers and patents. 

This course introduces the fundamental properties and characteristics of solid-state materials and structures used in modern semiconductor devices and integrated circuit (IC) technologies. 

Topics include Solid-State Electronics and applications, Crystal structure of solids, Introduction to quantum mechanics, Introduction to the quantum theory of solids, Semiconductor in equilibrium, Carrier transport phenomena, Excess carriers in semiconductors, The pn junction. 

This course analyzes the changing roles and functions of museums in a digital era. Students examine virtual museums, mobile applications, e-learning, and digital strategies. We also explore trends and horizons of museum technology to shape a museum of the future. Students complete article reviews and a project for a better understanding of the museum of our age. 

Topics include What is a museum, Museums in the digital age, Museum informatics, Digital collections management, Digital preservation, 3D applications in museums, Interactive museums, Case studies, Trends, HCI in the museum context, Virtual museums.