Teaching Experience
Geopolitics and Global Business Environment (EMBA level)
This course equips senior executives with a data-driven and geoeconomic framework for navigating an era
of intensifying geopolitical disruption. Drawing on cutting-edge empirical research, the course examines
the structural shift in global supply chains from efficiency-first (just-in-time) to resilience-first (just-in
case) paradigms and uses quantitative analysis to uncover the "shadow routes" and hidden dependencies
forged under the U.S.-China trade war through intermediaries such as Vietnam, Mexico, and India. Topics span macro-level geopolitical dynamics—including U.S. national security strategy, sanctions regimes, and
the geoeconomics of international institutions—and micro-level management tools, including the use of
large language models (LLMs) to monitor geopolitical risk and identify ESG compliance traps. Through
in-depth case studies of BYD, TikTok, SHEIN, and Lenovo, students develop the strategic acuity to
formulate precise hedging strategies, manage global stakeholders, and identify opportunities amid
economic realignment.
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Strategic Management of Innovation (EMBA level)
This course is designed to provide senior executives with a systematic and action-oriented framework for
innovation strategy, integrating economic theory, data intelligence, and frontier business models into a
unified decision-making architecture. Drawing on growth theory from Solow to Romer, platform
competition, game-theoretic timing, and real options valuation, the course builds a rigorous analytical
foundation for understanding why and how innovation happens—and how to govern it strategically.
Students learn to harness alternative data sources, including global patent databases and high-talent
mobility flows, to generate competitive intelligence and identify emerging strategic opportunities before
they become widely visible. The course further decodes the innovation logic of world-leading firms such
as NVIDIA (servitization), BYD (supply chain finance), and SHEIN (data-driven agility), while exploring
disruptive technologies including LLMs, Agent AI, digital twins, stablecoins, and real-world asset
tokenization, alongside China's urban innovation ecosystems and regional development models. Guest
speakers from frontier industries and student group research presentations ensure that strategic insight is
translated into executable action.
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Advanced Topics in Global Operations and Supply Chain Strategies (DBA level)
This course offers doctoral students a rigorous and multidimensional understanding of global supply
chains—tracing their historical evolution, analyzing current structural pressures, and anticipating future
trajectories—with particular attention to China's pivotal and evolving role. The course examines the key
forces driving deglobalization trends, including the U.S.-China trade war, the COVID-19 pandemic, the
Russia-Ukraine conflict, and the rise of non-tariff trade barriers, exploring how these disruptions have
accelerated reshoring, nearshoring, and regional integration strategies. Students engage with empirical
research at the frontier of supply chain management and work on group projects using data visualization
tools to assess the impact of trade policy on global sourcing networks. Through case studies on corporate
supply chain restructuring and the overseas expansion of Chinese firms, the course develops students'
ability to think strategically about supply chain resilience and to generate original, evidence-based insights
relevant to both academic research and executive decision-making.
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Empirical Operations Management (PhD level)
This course offers doctoral students a rigorous and up-to-date foundation in empirical research methods for
operations management, with the dual goal of cultivating the ability to identify high-impact research
questions and to execute credible empirical designs. On the methodology front, the course covers the
modern causal inference toolkit—including difference-in-differences with staggered adoption and
heterogeneous treatment effects, instrumental variables, regression discontinuity designs, and synthetic
control methods—as well as machine learning for causal inference (double/debiased ML and causal forests).
Students are also trained in text-as-data and unstructured data methods, including large language models
(LLMs) for measurement, classification, and the analysis of managerial narratives from earnings calls,
news, and regulatory filings. Substantive topics span the core domains of OM—inventory management,
global supply chains, operations-finance interface, and sustainable operations—as well as frontier areas
including geopolitical risk and supply chain resilience, AI adoption in firm operations, platform and gig
economy operations, and the use of novel data sources.
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Quantitative Asset Management (MBA and MSc level)
This course equips students with a rigorous and contemporary toolkit for quantitative investing and data
driven asset management, bridging foundational theory with the frontier practices reshaping the industry.
11Core topics include empirical asset pricing (CAPM, multi-factor models), alpha factor construction and
validation using performance attribution, and portfolio optimization beyond mean-variance. A central
theme of the course is the revolution in alternative data and machine learning for investment: students learn
how satellite imagery, credit card transactions, supply chain shipping records, job postings, and web
scraped data are systematically transformed into tradable signals, and how gradient boosting, deep learning,
and transformer-based models are deployed for return prediction, earnings surprise forecasting, and
dynamic factor timing. The course also covers large language models (LLMs) applied to financial text such
as earnings call transcripts, 10-K filings, and analyst reports. Students further examine smart beta, ESG
integration, systematic macro and long/short equity strategies, and the operational infrastructure of
quantitative hedge funds. This course is ideal for students targeting careers in asset management, hedge
funds, or other quantitative strategy roles.
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FinTech and Its Application (MBA & MSc level)
This course provides a comprehensive and practice-oriented journey through the core building blocks of
modern financial technology, structured to take students from foundational concepts to real-world
applications. The curriculum covers the evolving FinTech landscape, the use of digital footprints and
alternative data in credit and investment decisions, automated platform lending, and quantitative investing
strategies informed by machine learning and supply chain alpha signals. Transformative technologies are
examined in depth, including Bitcoin and blockchain protocols, stablecoin mechanics, the tokenization of
real-world assets (RWA), and the application of large language models (LLMs) in financial services such
as robo-advising and trading signal generation. The course concludes with a critical examination of FinTech
regulation using the landmark Ant Financial case, and is assessed through hands-on group projects on
platform lending, fund performance analysis, and blockchain solution design, making it particularly
valuable for students pursuing careers in finance, technology, or platform economics.
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Business Intelligence and Its Applications (MSc level)
This course provides a comprehensive and hands-on introduction to modern business data analytics,
spanning the full pipeline from raw data to managerial decision-making. Core topics include data
preparation and visualization, supervised learning (linear and logistic regression, decision trees, random
forests, gradient boosting, and neural networks), unsupervised learning (clustering, dimensionality
reduction, and kernel methods), and causal machine learning. A significant portion of the course is
dedicated to natural language processing in the era of large language models (LLMs): students learn
transformer-based architectures, prompt engineering, retrieval-augmented generation (RAG), and fine
tuning techniques, with applications in sentiment analysis, document intelligence, and AI-assisted business
reporting. The course further covers graph neural networks for economic network analytics, explainable
AI (XAI) for model interpretability, and real-time/streaming analytics for dynamic business environments.
Python is used throughout, with hands-on exposure to industry-standard libraries including scikit-learn,
PyTorch, Hugging Face Transformers, and LangChain. Projects apply these tools to consumer product data,
financial time series, supply chain records, and Internet text, equipping students with the practical skills to
deploy state-of-the-art analytics in data-rich business contexts.
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Operations Management (MSc level)
This core course develops students' ability to design, manage, and continuously improve the operational
processes that underpin a firm's competitive advantage across dimensions of cost, quality, speed, and
variety—capabilities equally critical in manufacturing, retail, financial services, healthcare, and platform
based businesses. The course grounds students in enduring principles—demand forecasting, inventory
optimization, capacity planning, process flow analysis, Just-in-Time, risk pooling, and supply chain
coordination—while systematically extending these frameworks to the realities of modern operations: AI
driven demand forecasting and dynamic pricing, digital twin–enabled process optimization, omnichannel
fulfillment and platform operations, and sustainable and resilient supply chain design in an era of
geopolitical disruption and climate risk. Students critically evaluate both classic paradigms (Quick
Response, lean operations) and emerging practices such as servitization, real-time data-driven operations management, and the operational implications of generative AI adoption, developing the analytical and
strategic judgment needed to lead operational transformation in complex, uncertain business environments.