Korea, South

This course introduces the basic mechanisms of global management and basic concepts of international management.  

Topics include globalization; national differences in political, economic, and legal systems; differences in culture; the competitive environment; foreign direct investment; developed vs. developing markets; exporting, importing, and countertrade; global production and supply chain management; and global marketing and business analytics.  

This course examines international nuclear non-proliferation regimes and geopolitical situations as an important part in international peace and security. Via case studies, the course explores the history of nuclear weapon development and the establishment of nuclear non-proliferation regimes as well as nuclear disarmament agreements between the United States and Russia (formerly the Soviet Union) and the UN’s efforts to ban nuclear weapons. 

The course further addresses the history of nuclear weapon development, use, and related, resultant negotiations, treaties, and subsequent political impact upon and by various governing bodies throughout the world. The course situates and reviews cases in the geopolitical context: nuclear development and possession in India, Pakistan, and Israel; the Cuban Missile Crisis; and Iranian nuclear development.  

In addition, the possibility of further nuclear proliferation by Saudi Arabia, Egypt, Japan and Taiwan is discussed in relation to geopolitical interests, and North Korea's nuclear tests and South Korea’s nuclear option are debated in the context of North Korea’s nuclear threat and geopolitical strategy. 

This course introduces exciting new developments in advanced mathematics. The barriers between fields are being broken, many new unexpected applications are continually found, and out of this cross-fertilization, new kinds of mathematics are born. Topics are subject to change but may include various new advances of pure mathematics and logic, computational science and numerical analysis, fluid mechanics and geophysics, wavelets and signal processing, cryptology, quantum computation, mathematical biology (including bioinformatics, proteomics and neuroscience), intelligence science, financial mathematics and mathematical economics, and probability theory with various applications.  

This course explores generative artificial intelligence (GAI) and its applications. Students gain a comprehensive understanding of generative models, including deep learning architecture, and probabilistic models. The course covers theoretical foundations and practical implementations of generative AI algorithms. Students also engage in hands-on projects to apply generative AI methods. Topics include introduction to generative AI (overview of generative modeling, brief history of GAI, applications of GAI), probability theory and information theory, parameters estimation, latent variable models, variational inference (introduction), variational autoencoders (VAEs) - autoencoders - variational autoencoders (VAE) - conditional VAE - VQ-VAE v1, v2, generative adversarial networks (GANs) - introduction to GANs - GAN training, issues and solution - generative model evaluation, GAN variants: DCGAN, CGAN, WGAN, ProGAN and Style-GAN, GAN applications: image manipulation and editing, diffusion-based generative models - DDPM - DDIM, diffusion-based generative models - classifier guidance DMs - classifier-free guidance DMs - cascaded DMs - latent DMs, autoregressive generative models - MADE, PixelNN, language generative models - Transformer - GPT family, multi-modal generative models - DALL-E (DALL-E 2 and DALL-E 3) - stable diffusion, flow-based generative models - RealNVP, GLOW.

Prerequisite: Solid understanding of machine learning and deep learning principles - Proficiency in programming - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

This course introduces the recently emerging fields of decision-making neuroscience and neuroeconomics. Topics include the core elements of the brain mechanism related to decision-making, such as dopamine function, approach and avoidance circuits, value calculation, and the dilemma of stability versus flexibility. In addition, this course covers the neuroscientific model of decision-making and how to understand and apply it to various choices in everyday life. 

This course is intended for advanced-level undergraduate students who have completed an introductory psychology course and are familiar with basic terms in neuroscience and elementary statistics. 

The contemporary world is facing many challenges in promoting peace, justice and reconciliation, ranging from armed conflicts to social inequality, from environmental degradation to interfaith tensions. These complex challenges continue to afflict many parts of the globe. In this context, some fundamental questions may be asked: how do we define life?; What does it mean to live a life of integrity?; How does my life relate to just, sustainable and inclusive peace?; How do the ideas for making the world more just and peaceful shape our own lives and careers of purpose and vice versa? Seeking to explore these questions deeply, this course presents foundational theories behind peace and social justice and applies these concepts to specific fields of inquiry and practice, including: colonization, violence, oppression, racism, sexism, human trafficking, poverty, climate change and complex issues of peacebuilding, humanitarian aid and development. Various strategies and attempts to create social change for the greater good through different individual and organizational platforms are analyzed and assessed too. Throughout the course, students gain an understanding of the strengths and constraints on theory and practice in the context of the creation of a culture of “human flourishing”, particularly in post-conflict societies, and engage in a variety of topics with self-reflective approach.

This course buildes competency in understanding and interpreting a range of different research methods and results. Through the analysis of various research papers, students will need to use critical thinking and logical reasoning to either agree, disagree, or seek further clarification of conclusions provided in the research discussion sections.

This course explores the significant achievements of behavioral economics, focusing on theoretical and empirical evidence. Unlike traditional economics, which assumes human rationality and standard preferences to understand decision-making and behavioral changes, behavioral economics expands human decision-making models based on new insights from psychology and other fields and introduces the concept of policy design and evaluation based upon insights into human behavioral change. Students will gain a thorough understanding of the fundamental concepts of behavioral economics; be able to critically evaluate the traditional economic theories on human rationality; and be able to compare and analyze the usefulness and policy implications of behavioral economics with those of traditional economics. 

Prerequisites: Microeconomics, Basic Calculus 

This course introduces theories in critical communication and technology studies and applies these theories to contemporary debates around big data and AI, examining multiple and situated contexts of technology within mediated environments. The course invites students to delve deeply into critical perspectives and explore where and how data systems re-arrange and re-organize existing human practices.  

This course explores the transformative role of new information technologies (IT) and artificial intelligence (AI) in reshaping businesses. Its goal is to equip students with an in-depth understanding of essential economic principles that are crucial for operating and excelling in IT-enabled and/or AI-driven enterprises, while also highlighting the economic ramifications of IT and AI at various levels - including individual firms, broader markets, and society as a whole.  

Students will delve into competitive market analysis and examine IT-specific economic challenges related to pricing, bundling, information asymmetry, and uncertainty. In addition, the module covers AI-centric topics such as automated decision-making and the broader economic effects of AI technologies. Key economic issues associated with IT (such as competitive markets, pricing strategies, and bundling) and foundational concepts like user lock-in, switching costs, and the network effect are discussed. Participants also gain insights into the workings of AI-enabled businesses and address AI-specific economic concerns, including automated decisions, algorithmic bias, and the influence of AI technologies on the job market.