Teaching
I teach students to investigate communication through theory, research design, and computational methods. Across journalism and data science, they learn to turn a substantive question into evidence and an argument they can defend.
Students enter my courses with different disciplinary backgrounds and technical experience. Through staged projects, coding labs, case discussions, and peer feedback, they learn to choose methods, evaluate results, and explain their research decisions.
Courses at Missouri
Emerging Technologies
I rebuilt this course around generative AI and its role in journalism. Students examine credibility, ethics, professional responsibility, and platform governance through critical reading and guided experimentation. A weekly log of AI prompts and outcomes supports an independent research proposal intended for publication.
Strategic Communication Research Methods
Student teams move from secondary research to surveys, interviews, and a client report grounded in evidence. Individual assignments and peer feedback keep each student involved in the research decisions and the final recommendation.
Introduction to Natural Language Processing with Generative AI
Students use natural language processing to study social media, political communication, and news. The course covers text preparation, dictionary methods, embeddings, machine learning, information extraction, and large language models, alongside questions of measurement, law, and ethics. A staged project develops from an idea pitch into a research paper or study proposal.
Communication Network Analytics
Students connect network theory with the analysis of communication systems. They build and visualize networks, study centrality and communities, compare models, and examine prediction and diffusion using digital trace data. Each student designs an independent network analysis project.
How I teach
From concepts to evidence
I ask students to trace a claim back to its concept, measure, and data. They explain how their methodological choices support the interpretation of their results.
Learning through structured practice
Short exercises build toward larger research tasks. Coding labs, surveys, case analysis, and project checkpoints give students repeated opportunities to apply ideas, receive feedback, and revise their work.
Responsible engagement with AI
Students learn to use generative AI deliberately and evaluate the resulting work. My courses combine guided experimentation, disclosure and reflection, evaluation of model outputs, and selected assignments completed without AI assistance. These activities build practical fluency and judgment about when AI helps with a task.
Teaching design in practice
1. From a communication problem to a recommendation grounded in evidence
In Strategic Communication Research Methods, student teams move through a complete applied research cycle.
2. Responsible AI learning cycle
In authorized Emerging Technologies activities, students document how they use an AI system and evaluate the resulting output before deciding whether it is usable.
3. Two pathways from data to evidence for social science
Natural language processing
- Frame a communication question
- Select and prepare text data
- Choose and justify an NLP method
- Validate and interpret the output
- Develop a research paper or proposal
Communication network analysis
- Frame a relational question
- Construct and describe a network
- Analyze structure, communities, and dynamics
- Visualize and interpret the results
- Complete an independent network study
4. Curriculum map
| Course | Intellectual contribution | Methods and practices | Culminating work |
|---|---|---|---|
| Emerging Technologies | Critical evaluation of AI and technological change | Documentation of AI outputs, ethical analysis, research design | Proposal intended for publication |
| Strategic Communication Research Methods | Communication decisions grounded in evidence | Secondary research, surveys, interviews, mixed evidence | Client research report |
| Introduction to NLP | Text as evidence for social science | Preprocessing, embeddings, machine learning, LLMs | Research paper or proposal |
| Communication Network Analytics | Relational structure and information flow | Network construction, centrality, communities, dynamics | Independent network analysis |
Earlier teaching and invited instruction
Before joining Missouri, I taught discussion sections in Introduction to Mass Communication and Mass Media in Multicultural America at the University of Wisconsin–Madison. I have also delivered invited methods instruction on data collection from social media, computational text analysis, topic modeling, and word embeddings at Cornell University and the University of Missouri.