Teaching statement
I teach introductory programming and discrete mathematics. I have been instructor of record at a community college and at a research university, and the thing I hold constant between them is that the reasoning stays visible.
Before we choose a method, I want us to be able to say what the problem is asking and why that method applies to it. When an answer comes out wrong, we look for the first step where the work stopped matching the problem. That step gives a student something to do the next time they are stuck, which the final answer on its own does not.
The courses I have taught
CSC 151, Java Programming, at Central Carolina Community College, in Spring and Fall 2026. It is a first programming course, and the work of a term is getting a student to where they can read what they wrote and say why it does what it does.
CSC 226, Discrete Mathematics, at North Carolina State University, in Summer 2025 and Summer 2026. Counting, induction and predicate logic are the places where naming the problem before reaching for a rule matters most, and they are where I spend the most time.
CSC 216, Software Development Fundamentals, at North Carolina State University, in Summer 2024.
CSC 484/584, Game AI, as a guest lecturer in Fall 2026.
Teaching students to read what a chatbot gives them
My discrete mathematics teaching includes a session on examining what a chatbot produces. Students work through three procedures: checking a proof, strengthening a prompt, and working a problem alongside a chatbot. The label for each step appears in the reference boxes, the worked examples and the practice tasks, so a student can connect an action to the reason for it.
The assignments around that session are posted in full, with the lecture and the exercises. Course materials
What the materials say about themselves
The published lecture carries a note correcting its own simplified account of how a language model is trained, and points to a more careful one. The materials also record that the procedures were taught inside an ongoing study, and that their presence in a course does not establish that they improve learning. I want students to be able to tell a procedure that is taught from a result that is established.
Evidence
In 2026 I received Central Carolina Community College's Excellence in Teaching Award for STEM, for my teaching in CSC 151 during Spring 2026. The process begins with student nominations and is confirmed by supervisors or colleagues. The rubric considers active learning, students' recognition of their own growth, support outside the classroom, and service, alongside course evaluations. The student nomination that began it is quoted in full on my teaching page.
Teaching and research
My research asks the question my teaching does: what a student does with help once they have it. I study how undergraduates use generative AI while learning, with an interest in discrete mathematics and proof writing. The classroom study behind the discrete mathematics materials ran in a course I was teaching, using two perturbed assignments with an AI literacy session between them. What the data shows changes the next term's assignments.