Korea Advanced Institute of Science and Technology (KAIST)

The course begins with quantum logic and examines how quantum advantages can be achieved in communication and computational tasks. Examples of quantum algorithms and quantum protocols are provided. Known approaches to implement quantum information processing are explained. Topics include Quantum logic state, dynamics, and measurements and observations, Quantum bit, fundamental theorems, Quantum logic gates and information processing, Quantum protocols, Quantum algorithm 1: the Deutsch algorithm, Entanglement, Quantum protocol 2: Pseudo-telepathy, Quantum computing concepts, Nonlocality.  

This course covers the fundamental concepts and principles that shape modern computer networks, helping students understand how the Internet is designed and is being operated in practice, and the course prepares students to think about current issues. Class content is introduced top-down, starting with the applications that are most familiar to students, such as the Web and e-mail. Students gain a hands-on perspective by writing their own simplified versions of popular Internet protocols. 

Topics include HTTP and FTP, Socket programming, Transport layer overview and Reliable data transfer, TCP and congestion control, Network layer, Virtual circuits, Datagrams, Forwarding, IP and ICMP, Routing algorithms and protocols, Link layer, Error detection and multiple access, ARP, Ethernet, hubs and switches, Wi-Fi, Email security and SSL, IPSec, VPN, Wireless LAN security, and more. 

Prerequisite: EE209 Programming Structure for Electrical Engineering or similar  

This course covers the basic theory and principles of signal and systems, which play important roles in various communications and information systems analysis and control. Topics include linear time-invariant system theory, Fourier analysis, continuous and discrete Fourier transform, time and frequency characterization, sampling theory, Laplace transform, and the Z-transform. After completing this course, the students are expected to be familiar with system models, signal analysis, and filtering techniques that are essential for more advanced communications and information systems. 

This course covers basic electromagnetic theory. The course discusses electrostatics, magnetostatics, electrodyanmics, and electromagnetic waves. Textbook: Elements of Electromagnetics 6th Edition, by G.P. Matthew N. O. Sadiku (Oxford University Press, 2015) 

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