I love teaching and care about my students. Over the years, I have invested tremendous time and effort in improving my teaching skills and giving students a good learning experience. I have received many teaching awards, including the Carlson School of Management Outstanding Teaching Award twice in 2017 and 2021, the MABA Faculty of the Year Award in 2023, and the Global DBA Teaching Award in 2024.
During my time at Carlson, I have developed a diverse teaching portfolio, with courses at all levels from undergraduate to master, PhD, and executive education. I am particularly experienced with teaching large, required courses such as IDSC 3001 on Information Systems and Digital Transformation and executive courses such as GDBA 7202 Innovation through Emerging Technologies, both of which are challenging courses to teach.
My teaching philosophy centers around three principles:
1) Critical thinking and mindset shift.
Students do not come to class as blank slates. They bring with them preconceived assumptions and beliefs. Part of a teacher’s job is to help students reconsider and revise these misperceptions. I consider my teaching successful if my students leave the class with a different mindset about at least one thing. For example, they may enter my class thinking Artificial General Intelligence (AGI) is in its infancy or on the horizon, especially after recent experiences with Generative AI. They will graduate from my class knowing the difference between strong AI and weak AI and the fact that today's AI applications, no matter how powerful they seem, are still a weak or narrow form of AI and do not yet have general intelligence.
2) Active learning and less is more.
I am a big believer of active learning and "less is more." I would rather cover fewer topics with greater depth, than bombarding students with lots of information that will not stick. Humans have short attention spans, and we learn better when we can actively engage with the materials, than passively listening to the teacher. I divide class time into multiple sessions of 15 to 20 minutes, and mix lectures with in-class exercises and discussions. I am especially good at facilitating open discussions around complex thought-provoking topics. I see myself more as a designer and facilitator of learning experiences, than simply a disseminator of knowledge.
3) Knowing the audience.
An effective teacher needs to meet students where they are. My role model in teaching is Richard Feynman, who is not only a great physicist but also an excellent teacher who can make complex concepts accessible to students. Every time I teach a new subject or to a new audience, I try to find out what students already know and what they are interested in learning. Empathy is one of my personal strengths, which helps me see things from the student’s perspective and explain things in ways that they can understand. I am good at using analogies and real-world examples to help students relate to and understand technical and abstract concepts.
Here are a list of courses that I have developed and taught over the years:
Digital Transformation and Emerging Technologies (Executive and DBA Levels)
This course is designed to help top executives to become tech savvy and prepare their organizations for the rapidly changing technological environments. We start with a deep dive into IT as a disruptive force and how companies and individuals can be innovative. We cover the fundamentals of business analytics and how emerging technologies such as the artificial intelligence, blockchain, and quantum computing can continue to transform and shape our businesses, lives, and society.
AI for Competitive Advantage (Master Level)
This course is about artificial intelligence and its business applications. Through a combination of readings, case discussions, exercises, and projects, we will explore the following questions:
- What is artificial intelligence? How do machines learn? What are major machine learning techniques?
- How do machines play games, recognize images and speeches, translate, answer questions?
- What are convolutional neural networks? What is reinforcement learning?
- What are business applications of AI in finance, accounting, human resources, marketing, sales, etc.?
- What is explainable AI? What are the ethical risks and challenges associated with AI?
- What is the impact of AI on humanity? Will or when will superintelligence emerge?
Web 2.0: The Business of Social Media (MBA and Undergraduate)
This course focuses on key social media technologies and their business applications in marketing, advertising, innovation, and collaboration. How to design and implement a social media marketing strategy or campaign? How can businesses effectively respond to social media crises? How can managers use to social media to promote employee engagement and collaboration? What are the risks and "dark sides" of social media. Students will read, think, and discuss these questions through a combination of readings, cases analyses, and hands-on projects.
IDSC 3001 Information Systems for Business Processes and Management (Undergraduate Level)
This is a broad survey course required of all Carlson School undergraduate students. It covers topics including enterprise resource planning (ERP), customer relationship management (CRM), electronic commerce, social media, privacy, security, IT infrastructure, database, artificial intelligence, Internet of Things, etc. The course equips business students with a comprehensive and solid understanding of digital technologies and how they have and will continue to transform businesses.
Research Seminar on Emerging Technologies: Artificial Intelligence, Blockchain, & Virtual Reality (PhD Level)
This is a PhD Seminar on the latest research on emerging technologies such as artificial intelligence, blockchain, metaverse, and social media. Example topics include AI transparency, AI aversion, human-robot interactions, designs and mechanisms of blockchain, the impact of augmented reality and virtual reality, peer production, and online social networks. We will be reading and discussing book chapters and articles from multiple disciplines including but not limited to information systems, human-computer interaction, management, communication, and computer science.
Research Seminar on Social Media and Online Communities (PhD Level)
This is a PhD seminar to expose students to theories and methods related to social media and online communities. We cover key topics in motivation, identity, collaboration and innovation, social networks, community dynamics and evolution, electronic word-of-mouth, and risks associated with social media. Through readings and class discussions, students will learn how to read academic papers, how to synthesize papers to understand the big picture, how to motivate a research question, how to theorize, and how to write papers.
During my time at Carlson, I have developed a diverse teaching portfolio, with courses at all levels from undergraduate to master, PhD, and executive education. I am particularly experienced with teaching large, required courses such as IDSC 3001 on Information Systems and Digital Transformation and executive courses such as GDBA 7202 Innovation through Emerging Technologies, both of which are challenging courses to teach.
My teaching philosophy centers around three principles:
1) Critical thinking and mindset shift.
Students do not come to class as blank slates. They bring with them preconceived assumptions and beliefs. Part of a teacher’s job is to help students reconsider and revise these misperceptions. I consider my teaching successful if my students leave the class with a different mindset about at least one thing. For example, they may enter my class thinking Artificial General Intelligence (AGI) is in its infancy or on the horizon, especially after recent experiences with Generative AI. They will graduate from my class knowing the difference between strong AI and weak AI and the fact that today's AI applications, no matter how powerful they seem, are still a weak or narrow form of AI and do not yet have general intelligence.
2) Active learning and less is more.
I am a big believer of active learning and "less is more." I would rather cover fewer topics with greater depth, than bombarding students with lots of information that will not stick. Humans have short attention spans, and we learn better when we can actively engage with the materials, than passively listening to the teacher. I divide class time into multiple sessions of 15 to 20 minutes, and mix lectures with in-class exercises and discussions. I am especially good at facilitating open discussions around complex thought-provoking topics. I see myself more as a designer and facilitator of learning experiences, than simply a disseminator of knowledge.
3) Knowing the audience.
An effective teacher needs to meet students where they are. My role model in teaching is Richard Feynman, who is not only a great physicist but also an excellent teacher who can make complex concepts accessible to students. Every time I teach a new subject or to a new audience, I try to find out what students already know and what they are interested in learning. Empathy is one of my personal strengths, which helps me see things from the student’s perspective and explain things in ways that they can understand. I am good at using analogies and real-world examples to help students relate to and understand technical and abstract concepts.
Here are a list of courses that I have developed and taught over the years:
Digital Transformation and Emerging Technologies (Executive and DBA Levels)
This course is designed to help top executives to become tech savvy and prepare their organizations for the rapidly changing technological environments. We start with a deep dive into IT as a disruptive force and how companies and individuals can be innovative. We cover the fundamentals of business analytics and how emerging technologies such as the artificial intelligence, blockchain, and quantum computing can continue to transform and shape our businesses, lives, and society.
AI for Competitive Advantage (Master Level)
This course is about artificial intelligence and its business applications. Through a combination of readings, case discussions, exercises, and projects, we will explore the following questions:
- What is artificial intelligence? How do machines learn? What are major machine learning techniques?
- How do machines play games, recognize images and speeches, translate, answer questions?
- What are convolutional neural networks? What is reinforcement learning?
- What are business applications of AI in finance, accounting, human resources, marketing, sales, etc.?
- What is explainable AI? What are the ethical risks and challenges associated with AI?
- What is the impact of AI on humanity? Will or when will superintelligence emerge?
Web 2.0: The Business of Social Media (MBA and Undergraduate)
This course focuses on key social media technologies and their business applications in marketing, advertising, innovation, and collaboration. How to design and implement a social media marketing strategy or campaign? How can businesses effectively respond to social media crises? How can managers use to social media to promote employee engagement and collaboration? What are the risks and "dark sides" of social media. Students will read, think, and discuss these questions through a combination of readings, cases analyses, and hands-on projects.
IDSC 3001 Information Systems for Business Processes and Management (Undergraduate Level)
This is a broad survey course required of all Carlson School undergraduate students. It covers topics including enterprise resource planning (ERP), customer relationship management (CRM), electronic commerce, social media, privacy, security, IT infrastructure, database, artificial intelligence, Internet of Things, etc. The course equips business students with a comprehensive and solid understanding of digital technologies and how they have and will continue to transform businesses.
Research Seminar on Emerging Technologies: Artificial Intelligence, Blockchain, & Virtual Reality (PhD Level)
This is a PhD Seminar on the latest research on emerging technologies such as artificial intelligence, blockchain, metaverse, and social media. Example topics include AI transparency, AI aversion, human-robot interactions, designs and mechanisms of blockchain, the impact of augmented reality and virtual reality, peer production, and online social networks. We will be reading and discussing book chapters and articles from multiple disciplines including but not limited to information systems, human-computer interaction, management, communication, and computer science.
Research Seminar on Social Media and Online Communities (PhD Level)
This is a PhD seminar to expose students to theories and methods related to social media and online communities. We cover key topics in motivation, identity, collaboration and innovation, social networks, community dynamics and evolution, electronic word-of-mouth, and risks associated with social media. Through readings and class discussions, students will learn how to read academic papers, how to synthesize papers to understand the big picture, how to motivate a research question, how to theorize, and how to write papers.