Teaching Experience

Empirical Operations Management (PhD level)

This course is designed to provide an overview of empirical research in operations management for PhD students. It aims to teach students how to discover interesting research topics and provide exposure to the various types of empirical methods such as DID, IV, RDD, large language model (LLM), etc. Readings and discussions will cover topics such as inventory, supply chains, operations-finance interface, sustainable operations, etc. The course objectives are designed to progressively enhance students' skills and understanding of empirical research within operations management. The basic objectives are 1) to broaden students’ horizons on research questions and 2) to introduce a range of empirical methods and paradigms. The advanced objectives include 1) to develop students' ability to formulate impactful research questions and 2) to develop students' skills in evaluating empirical research.​​

 

Machine Learning for Business Research (PhD level)

This course provides a broad introduction to machine learning with applications using business research data. The topics of the class include supervised learning, unsupervised learning, and natural language processing. The course also includes guest lectures with a focus on cutting-edge machine learning research in the business domain. Python programming is used throughout the course. There are several course projects implementing various machine learning algorithms on consumer product data, financial time-series data, and web-scraped text data for business analysis. The goal is to equip research-degree students with practical hands-on exposure to utilize machine learning packages in their future careers.​

 

Global Operations and Supply Chains (DBA & EMBA level)

This course provides students with a comprehensive understanding of the historical evolution, current challenges, and future trends shaping the global supply chain landscape. It aims to equip students with the strategic insights and analytical skills necessary to navigate the complexities of global operations in the modern business environment. Drawing from the rich tapestry of global supply chain development, the course will delve into the historical milestones from the early 20th century to the advent of Industry 4.0, highlighting the pivotal role of technological advancements in shaping supply chain efficiency and resilience. Students will explore the impact of geopolitical challenges, such as trade wars and the COVID-19 pandemic, on supply chain disruptions and the subsequent reshaping of global trade dynamics. Through case studies, students will examine the role of government policies in deglobalization trends, including the strategies of onshoring, nearshoring, and regional integration.​​

 

Financial Data Modeling and Analysis (MBA level)

The objective of the course is to familiarize students with theories and techniques of active quantitative trading strategies and the asset investment industry under the framework of financial data modeling and analysis. The course will cover a wide range of topics in quantitative investing and asset management, including quantitative stock selection models, portfolio construction methods, portfolio performance evaluation, institutional investors and investment products, and new trends in the asset management industry, such as the rise of smart beta and socially responsible investing, etc. We will read research studies and integrate the insights into our investment process. This course is designed for students with an interest in quantitative investing, either personal or professional. It should be particularly useful to students interested in careers in asset management, equity research, hedge funds, or investment banking.​

 

FinTech Application (MBA & MSc level)

This course is designed to introduce students to the fundamental building blocks of various financial technologies and real-world applications through case studies and quantitative analyses. Students will learn the essential components of technology-driven financial applications, including the disruptive forces of digital payment, modern investment theory-driven robot investing, innovative financing and funding schemes (crowdfunding, peer-to-peer lending, etc.), and blockchain (applications such as tokenization). This course will also extend the concept of financial technologies to insurance (InsurTech), real estate (RETech), regulation (RegTech), and supply chain financing. Notably, we will discuss the complex challenges in the changing regulatory landscape.​

 

Business Intelligence and its Applications (MSc level)

This course provides a broad introduction to business data analytics using statistical tools and machine learning techniques. The goal is to generate actionable business decisions and managerial insights. The topics of the class may include data preparation, data visualization, exploratory analysis, supervised learning (linear regression, logistic regression, neural networks, support vector machines, tree and random forest, etc.), unsupervised learning (clustering, dimensionality reduction, kernel methods), natural language processing, and economic network analytics. Python is used throughout the course. Course projects may implement various business analytics tools on consumer product data, financial data, and Internet text data for business analytics. Students will learn key data analytics techniques and machine learning packages to address existing business problems in today’s information-rich environments.​​

 

Operations Management (MSc level)

This core course focuses on understanding levers for structuring, managing, and improving a firm’s recurring business processes to achieve competitive advantage in customer responsiveness, price, quality, and variety of products and services. These levers are as applicable to banks, hospitals, and brokerages, for example, as to traditional manufacturing and logistic firms. The fundamental principles underlying state-of-the-art practices, such as Quick Response, Just-in-Time, Pooling, and Risk, are explored so that students learn to critically evaluate these and other operational improvement programs.​​