Korea Advanced Institute of Science and Technology (KAIST)

This course introduces key concepts and analytical tools for understanding science and technology as social enterprises. Students examine classical philosophical debates—such as the demarcation problem—and analyze how social systems, institutional norms, and cultural contexts shape the work of scientists and engineers.  

The course explores motivations and incentives that drive knowledge production, as well as the collaborative and competitive structures that organize research. Building on this foundation, the course asks practical questions about how to promote science and technology through effective governance, economic analysis, and policy design.  

A distinctive feature of this course is its applied project structure. Students take on two roles over the semester: first, acting as a funding agency by drafting Requests for Proposals (RFPs) on pressing science policy issues; second, acting as policy researchers by responding to a peer’s RFP with a complete policy study.  

This process mirrors real-world science policy cycles, from setting priorities to producing actionable recommendations, and will push students to think both strategically and analytically. By the end of the course, students will have a critical understanding of how science and technology are constructed, organized, and sustained, as well as hands-on experience in research design, policy analysis, and communication skills directly transferable to real-world science policy work. 

This course covers the tools required to evaluate and carry out empirical data analyses and introduces students to various regression methods that empirical researchers (economists, social scientists, data scientists, etc.) use for estimating, testing, and forecasting causal relationships.  Frontier research papers with various economic data sets are covered, and the course discusses how machine learning and econometrics can be used together to improve causal inference.  

Topics include basic regression models, advanced topics in panel data, time series analysis, difference-in-differences models, and discrete choice models 

Prerequisites: Basic knowledge of linear algebra, probability, and statistics is expected. If you are not sure whether you meet the prerequisites, please consult with the instructor.  

This course uses a human-centered lens to examine security and privacy, focusing on how design and research can create solutions that people can understand, trust, and use.  

Security and privacy are as much about people as they are about technology. Many failures arise not just from a lack of technical capability, but from mismatches with how people think, behave, and interact in their everyday contexts. 

Students engage with real-world topics ranging from authentication and security warnings to deceptive patterns, AI privacy, and privacy and security challenges in sensing environments, while learning foundational methods in user research and usable security and privacy evaluation. Through critical readings, class discussions, and hands-on projects, students develop skills to understand and design for human factors in security and privacy contexts. 

Key themes include: 1) Human-centered research methods for security and privacy, 2) Usable security tools, access control, and warnings 3) AI-enabled security and privacy challenges, 4) Sensing environments and security/privacy issues, and 5) Ethics and social implications in security and privacy 

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.