Graduate

This course is related with the students graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation.

This course is related with the students graduate thesis and dissertation. As such, students should be actively working in a laboratory setting and gaining experience through hands-on experimentation.

This course will provide students with analytical and decision making skills through a variety of topics in statistics and optimization modeling. Underlying theory for statistical analysis and its business applications will be emphasized. This helps students evaluate and handle business situations with statistics in mind. As a result, students will be well prepared to describe and analyze data for decision makings in business fields such as marketing, operations, and finance. This course aims to teach students programming techniques for managing, and summarizing data, and reporting results.

Data mining is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. The basic data mining techniques and their use in a business context will be addressed. Furthermore, an advanced topic in data mining (i.e. process mining) will also discussed in the class.

This course provides an introduction to the optimization problems, algorithms and techniques, emphasizing basic methodologies and the underlying mathematical structures. The main subjects covered include basic linear programming, nonlinear programming, network flow problems, dynamic optimization, and applications.

Derived from domestic and global competition, firms in many industries seek to create innovative ways to move products from raw materials through the manufacturing process to customers more efficiently and effectively. Such innovation has been facilitated by the development of information technology. The firms redesign their supply chains to collect, process, transmit, share, and use a large amount of information with efficacy. Still others are focusing on cooperative relationships among all the players in the value chain and bypassing unneeded stages. This course examines many of the recent innovations in this area with an emphasis on technologies.

Disciplined thought is often based on analytical models: simplified, quantitative depictions of a complex reality that allow you to focus your attention on a few key issues. Management runs on numbers and models. This course covers the strengths and weaknesses of these quantitative models.
Furthermore this course focuses on models built using spreadsheets and, in particular, concentrates on optimization of spreadsheet models. Finally this course will instruct students on the use of discrete event simulation to model phenomena subject to random influences.

Service systems in transportation, retail, healthcare, entertainment, hospitality, and other areas are configurations of people, information, organizations, and technologies that operate together for specific functions and values. One difficulty in engineering and managing complex service systems is the lack of data required to monitor and improve the system elements. However, with recent advances in sensing technologies, various kinds and massive amounts of data can be collected from the elements of the service system, such as people and physical objects. This advancement contributes to unlocking the limitations of engineering and managing service systems. In this course, we will learn and apply concepts and methods for engineering and management of service systems with various types of data. Through assignments and a term project, the students will develop their own cases of service systems engineering and management.

The objective of this course is to teach fundamental methods about anomaly and change detection in a process or an environment. Topics covered include the univariate and multivariate analysis for continuous and discrete data, risk adjustments, data pre-analyses (such as dimension reduction), and scan statistics. This course is designed for master’s students in the engineering and statistics fields to learn about anomaly and change detections in terms of the basic concepts and practical tools. Also, it will help doctoral students in both fields broaden their knowledge base and get exposed to new applications.

Production is more than manufacturing – it encompasses everything from R&D to design, consumer behavior and end-of-use cycles. Emerging technologies are transforming the world of production, enabling more efficient processes and creating new value for industry, society and the environment.Smart Factory is a mechanism enhancing manufacturing innovation. Korea and other global leading countries are accelerating the progress of smart factory. The class will review strategy of smart factory and compares with other global leading countries. This class brings together key enablers such as IIOT, AI/Cognitive, Big Data, Advanced Robotics, DPS/Digital Twin, 3-D printing, and Cloud can transform the industry and studies various case study to accelerate inclusive technology while stimulating innovation, sustainability and employment. The class evaluates pilot study examining the latest approaches in skills development, drives improvements in partnerships and informs business model transformations and next generation industrial development strategies.

The 21st century has been the golden era of machine learning, and the GPU-based deep learning algorithms becomes an indispensable tool in both science and industry. This course introduces the fundamentals of deep learning. The objective of this course is to help students to understand the basic operation and architecture in modern AI research. Topics for the course include convolutional networks, recurrent neural networks, and attention mechanisms.

This course will cover the latest research trends in technology management (TM) from an IE perspective. The course materials are research papers published in the prestigious journals such as Research Policy, Technovation, Technological Forecasting and Social Change, and R&D Management. This course consists of two parts. In the first part, we will discuss research streams in the field of TM. In the second part, we will intensively study major models and methods that have been widely employed in TM literature. Students are expected to develop independent research capabilities including identification of research opportunities, building sound theoretical base, and choice of rigorous methodologies.

Real analysis is fundamental to many of the other courses in applied mathematics. Topics include metric spaces, Banach spaces, measure theory, and the theory of integration and differentiation.

Basic and advanced theories in probability and stochastic processes will be covered including expectation, conditional probability, law of large numbers, central limit theorem, markov chains, martingales, and Brownian motions.

This course emphasizes the development of basic numerical algorithms for common problems formulated in science and engineering. The course covers interpolation and approximation of functions, numerical differentiation and integration, numerical solutions of ordinary differential equations and direct and iterative methods in linear algebra.

This course covers the theory of the classical partial differential equations, the method of characteristics for first order equations, the Fourier transform, the theory of distributions in Sobolev spaces, and techniques of functional analysis.

Finite difference methods for solving ordinary and partial differential equations. Fundamental concepts of consistency, accuracy, stability and convergence of finite difference methods will be covered. Associated theory will be discussed.

This course provides tools to characterize qualitative properties of linear and nonlinear dynamical systems in both continuous and discrete time. The course covers stability analysis of differential equations, Hamiltonian systems, Pointcare mapping, and Reduction methods.

This course provides concise introductions to mathematical methods for problems formulated in science and engineering. Some selected topics are functions of a complex variable, Fourier analysis, calculus of variations, perturbation methods, special functions, dimension analysis, tensor analysis.

Brownian motion, Ito’s rule, stochastic integrals, and stochastic differential equations as well as their numerical simulations are covered. Application to chemistry, finance and partial differential equations will be also included.

This course provides fundamental techniques in scientific computation with an introduction to the theory and software of the topics: Monte Carlo simulation, numerical linear algebra, numerical methods of ordinary and partial differential equations, Fourier and wavelet transform methods. This course may involve numerical coding assignments and some use of software packages.

This course is a beginning of the mathematics that includes the rigorous theories of differentiation, integration, sequences, infinite series, and limit of functions. These subjects are studied in the context of real numbers, complex numbers, and real and complex functions.

This course introduces graduate students with current and special topics in Management Engineering.

This course introduces graduate students with current and special topics in Management Engineering..

This course introduces graduate students with current and special topics in Management Engineering.

This course introduces graduate students with current and special topics in Management Engineering.

This course introduces graduate students with current and special topics in Management Engineering.

The course offers graduate level students a systematic presentation of microeconomic theories. The use of mathematics to formulate and analyze economic models facilitates a more rigorous and thorough mastery of microeconomic theory. Mastery of these basic models of microeconomic analysis will provide students with the essential tools for solving a wide variety of applied economics and public policy problems. Topics will include consumer choice, firm behavior, market structure, game theory, factor markets and general equilibrium.

This course aims to provide students a basic understanding of theoretical foundations of macroeconomics at the graduate level. This course introduces basic macroeconomic models that help students understand the interactions of key macroeconomics variables (output, prices, and employment) and the impact of macroeconomic policies. The topics will include economic fluctuation, economic growth, monetary and fiscal policies.

In this course, students will learn key issues and perspectives of scientific research methodology. The primary objective of this course is to provide the theoretical foundations and practical skills to effectively apply qualitative and quantitative research methods in business disciplines. It will help students formulate research questions, do independent literature research, analyze/interpret qualitative and quantitative data, and establish evaluation criteria. Further, the course helps students develop their ability to collect data in an appropriate manner.

This course aims to introduce students to quantitative techniques commonly used in economic analysis and research. This course focuses on the application of statistical methods to the testing and estimation of economic relationships. Students will be introduced to econometric tools of analysis, and will be prepared to perform analytical and statistical work in economics and other applied research areas.

This course provides the basic concepts in statistics and applications to economics and management areas. It covers the use of multivariate normal sampling theory, linear transformations of random variables, and multi-sample tests, partial and multiple correlations, multivariate ANOVA and least squares, principal components analysis, and specially related topics.

This course is designed to provide students with advanced-level probability models with various applications required for graduate level research. The subjects covered in the course include the basic concepts in probability, discrete and continuous distributions, sampling distribution theory, and other related probability theories.

The purpose of this course is to enable students to reason about the role of ethics in trading and contracting in the energy commodity trading and financial market. In the course, students will participate in a series of case study discussions, focusing on analyzing the issues in moral terms and then making decisions and developing a set of reasons why the decision can ethically justified.
During the discussion, additionally, students will think about the impact of their financial transactions on stakeholders and societies.

The purpose of this course is to extend knowledge to the state-of-the-art R&Din real scientific fields; and to get indirect experience by contacting experts in various fields. Students and professors can exchange their own ideas and information to reach creative and fine-tuned achievements through the seminars.

This course covers the challenges embedded in attempting to benefit from both incremental or routine innovation and more radical or revolutionary changes in products and processes. It also highlights the importance of innovation to both new ventures and to large established companies, and explores the organizational, economic and strategic problems that must be understood to ensure innovation is a long term source of competitive advantage. Based on the knowledge about the problems identified, students will learn to analyze the problems, and discuss and investigate the strategies to overcome the problems.

Successful companies are transforming themselves towards innovation models that involve everyone in the organization. Leaders who understand how to enable a cadence of innovation though/along with others will create a competitive advantage. In this class students will learn and apply three levels of knowledge: 1) innovation practices of global organizations, 2) how to embed innovation in an organization through business processes and core competencies, 3) capability tools to create a pipeline of innovation.

This course provides students with a solid foundation in the research area of entrepreneurship and strategic issues of emerging and growing-stage ventures. It provides students with a view of entrepreneurship as a process of economic or social value creation by discussing the perspectives of conceptualizing and understanding of entrepreneurship for starting and managing a sustainable business. The topics include the distinct phases of opportunity recognition, entrepreneurial creativity and innovation. assembly of the financial and human resources needed to exploit the opportunity, launching the new venture, evaluating the challenges of growth and strategies for firm growth.

The goal of this course is to survey the major theoretical perspectives in organization theory (OT).
Organization theory is currently one of the liveliest areas in all of management studies in part because of the importance of understanding organizations and their environment and in part because of the challenges to traditional theory that have emerged over the past 20 years. This course is designed to acquaint you with many of the core theoretical underpinnings of current organizational theory and help you apply them to real world settings. It examines the major theoretical approaches to organizations including organizational economics, resource dependence perspective, network theory, organizational ecology, and institutional theory and their current applications.

Based on the discipline of social and managerial psychology, this course aims to cultivate mindsets and building skills to understand how organizations and their members affect one another. This course covers the diagnosis and resolution of problems in organizational settings. Students will learn theories and research related to organizational problems by identifying individual motivation and behavior, decision-making, interpersonal communication and influence, small group behavior, individual, and inter-group conflict and cooperation.

Innovation increasingly plays a critical role in generating both economic growth and sources of competitive advantage. This course focuses on how firms (both new and old) can create and capture value from product, process, and service innovations. To do so, this course will introduce students to new tools and frameworks for examining both new and old problems related to innovation and technological change. This course consists of a mix between lectures and case studies, with an emphasis on class discussion and debate. While most of the case studies in class will focus on technology-oriented contexts, many of the insights developed during this course will be highly applicable to firms in non high-tech industries as well. A mastery of the tools and frameworks developed in this course will be useful to executives, consultants, entrepreneurs, government officials, investors, and any manager responsible for the introduction and implementation of new products or services.

The course, Intellectual Property Management, focuses on intellectual property from the perspective of ‘why’ and ‘how’ for participants who have already covered the basics of ‘what’ Intellectual Property is.

This course as research seminar has three broad objectives: (1) providing and overview of important work in the economics and sociology of technological change and technology policy, (2) analyzing the role of innovation in firm strategy and (3) giving the student an introduction to important elements of the new institutional economics. We will be particularly focused on the implications of historical, behavioral, institutional, and organizational perspectives, particularly as they apply to technology innovation. Based on classic and contemporary readings we will examine debates on the drivers of the changes in institutions, organizations, and technology from the variety of perspectives drawing on the growing literature in economic sociology.

This course will provide students with an opportunity to study advanced topics in IT and MIS fields, both theoretically and practically. Topics include enterprise information systems, e-business, IT architectures, database management and system development.

Firms are likely to use various techniques of data mining for credit ratings, fraud detection, database marketing, customer relationship management, investments, and logistics are. This course introduces methods for data mining that help managers recognize patterns and use electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. This course covers the techniques such as subset selection in regression, collaborative filtering, tree-structured classification and regression, cluster analysis, and neural network methods.

Information technology (IT) creates great opportunities for business by fundamentally reshaping products/services, operations, and business relationships. The course provides an in-depth economic analysis of the phenomena with managerial implications. It will cover economic value of information technology and its impact on industry and organizational structure. We will also examine such topics including, competition, pricing, bundling and versioning of information goods and network effects.

This course focuses on exploring and articulating the framework and methodology associated with investment in Information Technology to formulate and execute business strategy. This course will help students understand opportunities and challenges that firms confront in their IT investment decision making. Firms need to use IT to deliver business value. Today, IT is so closely related with business strategy and activities, and firms need strategy for IT management. Topics include IT values in business, IT impact on business model and on organizations.

Marketing Research and Analysis is designed to provide a conceptual framework and real-world applications for the role of research in managerial decision making. This framework will include a basic understanding of the research process, from the proposal stage to presenting the final results.
The course will discuss key elements and issues in marketing research: sources of data, data collection techniques, specific applications of research for decision making, and analytical approaches for understanding the implications of the data for marketing decisions.

Studies nature and determinants of consumer behavior: how different psychological characteristics and processes affect how people act when they buy, use, and experiences products or services. The impact of consumer decisions on the marketing strategies of organizations.

Develops framework for strategic marketing plan based on customer behavior, market segmentation, product positioning, product life cycle, market responsiveness, and competitive reaction. Analysis of complex marketing problems involving policy and operational decisions; emphasis on creative marketing strategy.

Analyzes marketing strategy across national boundaries, the problems of marketing within foreign countries, and the coordination of global marketing programs. Provides an analysis of marketing concepts and applications in a global environment, focusing on market management and cultural and institutional differences.

This course introduces graduate students with current and special topics in General Management.

This course introduces graduate students with current and special topics in General Management.

This course introduces graduate students with current and special topics in General Management.

This course introduces graduate students with current and special topics in General Management.

This course introduces graduate students with current and special topics in General Management.

This course is designed to provide a conceptual framework for understanding the field of corporate finance. The issues addressed in this course include time value of money, relation between risk and return, capital budgeting, and capital structure under certainty and uncertainty. This course will emphasize the logical structure of various theories and empirical evidence.

This course introduces the valuation models and risk management techniques used in options, futures and other derivative securities. It helps students understand derivative securities in detail by examining the structures of markets, analyzing pricing models and examining related empirical results.

This course provides students with a rigorous treatment of the core concepts of investment and their application to the study of financial markets. Topics include portfolio optimization and asset allocation, the theory of asset pricing models and their implications for portfolio management.

This course studies the fundamental theories and practice of corporate governance. Corporate governance involves a set of relationships between a company’s management, its board, its shareholders and other stakeholders. Corporate governance also provides the structure through which the objectives of the company are set, and the means of attaining those objectives and monitoring performances are determined. Topics include agency theory, separation of ownership and control, boards of directors, shareholder activism, as well as executive compensation.

Statistical programming such as SAS, R project, and Python is widely used in financial academic research as well as in financial industry. This course introduces basics of the statistical programming along with database management skills, and then provides applications of these tools to many financial economics problems such as optimal portfolio choice, risk managements and etc.

This course focuses on the nature and the role of financial institutions in various capital and financial markets. It will cover the financial system and financial service industry, and addresses the various issues concerning risk measurement and management of various financial institutions.

This course is designed to introduce students to basic issues of financial risk management including the definition of risk, measures of financial risk and the concept of financial risk management. It will focus on various risk management techniques to estimate value-at-risk. Practical problems for financial institutions and firms are discussed in class.

This course studies the mathematical and economic foundations for discrete and continuous time models in modern finance theory. It covers stochastic calculus, optimization techniques and models to analyze advanced issues in the multi-period portfolio theory, the arbitrage pricing theory, term structure of interest rates and the multi-period asset pricing theory..

This course introduces students to various empirical methods used in modern financial economics. The course focuses on (i) the empirical techniques most frequently used in financial market analyses; and (ii) the application of these techniques to actual market data. The list of topics includes, but is not limited to, statistical properties of asset returns and the efficient markets hypothesis; empirical tests of asset pricing models (CAPM); event studies; and other topics such as propensity scoring methods, variance ratio, autocorrelation, bootstrapping, etc.

This course is a cross-disciplinary field which covers mathematical and computational finance, statistics, and numerical methods that are useful for making structured financial instruments. Students will decompose and reconstruct several financial structured products..

This course is an introduction to empirical research in asset pricing. The main topic is the application of econometric methods in finance. Topics include tests of classic models of finance in the cross-section, predictability and excess volatility of equity returns, and systematic risk factors..

This course studies the main theoretical and empirical models used in market microstructure. Topics include mechanisms of how information is impounded in prices, sequential trade models, inventory control and empirical study of dealer inventories, market impact, as well as informed and strategic trading.

This course is an introduction to mergers and acuisitions(M&A) for finance major. Topics include major elements of the acquisition process including corporate strategy, valuation, due diligence, financing decisions, transaction structures, restructuring options, takeover defense, and role of institutional investors.

This course introduces private equity and venture capital as well as entrepreneurial finance. Topics include introduction of the private equity markets, the structure and objectives of private equity and venture capital funds, the analysis and financing of investment opportunities, and harvesting strategies for investments.

This course introduces fundamentals of energy markets, which are commodity markets dealing with the trade and supply of oil or other sources of energy. In particular, the first part of this course is focusing on crude markets and the second part is dealing with the refined product markets. Topics may include a historical perspective on the crude oil markets and crude price, crude oil production economics, refining technology, products demand and supply, and the role played by logistics and transportation to the cost of crude oil. Further, this course covers the clean fuels, climate change and their influences on crude price.

This course broadly consists of two major trading ways in energy markets: physical trading and derivatives (or paper) trading. Main topic of this course is trading mechanism in energy markets. Topics may include trading of commodities in an efficient and profitable manner, how commodity pricing mechanisms emerge, the role of the various market participants, the impact of liquidity of the different paper markets, and technical analysis for derivatives trading.

This course introduces financial reporting practices and financial accounting standards including IFRS (International Financial Reporting Standards) and US and Korean Accounting Standards. In addition, financial accounting theory will be discussed.

This course introduces and discusses contemporary auditing practices and theories. The discussion will focus predominantly on computerized auditing and audit of High Tech and Energy companies. GAAS (Generally Accepted Auditing Standards) of US and Korea will be examined.

This course will address contemporary issues in accounting such as IFRS, Balanced Scorecard, Strategic Cost Management, and Top Executive Compensation.

This course aims to provide a comprehensive understanding of the theoretical and practical applications of current information technology for accounting. Topics include the analysis and design of accounting information systems, accounting databases, and IS control and computerized auditing.

This course introduces and explores Research Methodologies in Accounting: Empirical, Archival, and Behavioral. Students will be expected to replicate major research papers using modified data sets.

This course introduces graduate students with current and special topics in Finance / Accounting.

This course introduces graduate students with current and special topics in Finance / Accounting.

This course introduces graduate students with current and special topics in Finance / Accounting.

This course introduces graduate students with current and special topics in Finance / Accounting.

This course introduces graduate students with current and special topics in Finance / Accounting.