Guiding Philosophy
The computational sciences are elegant and insightful, but often misconstrued as mere collections of facts and methods. Beauty doesn't lie in procedures or theorems, or even in the answers they provide, but in the intuition, the proofs, the clarity of understanding. Put more eloquently:
Science is built up with facts, as a house is with stones.
But a collection of facts is no more a science than a heap of stones is a house.
Content can be learned through mindless memorization of procedures and methodology, but the intuition—the beauty—is developed only through practice, critical assessment, and reflection. This is the cornerstone of my pedagogy.
Core Principles
Thinking over memorizing: We frame technical work as a practiced craft; students investigate why methods work so ideas become intuitive tools rather than isolated rules.
Concept-driven assessment: A standards-based, revision-friendly grading system rewards clear reasoning and communication; students revisit concepts until they can explain, as well as implement.
Active, reflective learning: Mini-lectures flow into group explorations and whole-class reflections; every student practices thinking aloud, making mistakes, and refining ideas.
Personalized context: Open-ended projects empower students to explore their own interests; from sports analytics to social justice, content is made personally relevant.
Belonging and community: Intentional group formation, frequent check-ins, and universally designed materials cultivate belonging and ensure equitable paths to success.
Courses Taught
I have taught 54 sections across 22 courses. Below are these course titles, in reverse chronological order since last taught:
- Current Courses
- Computational Data Science & Mathematics Seminar
- Introduction to Programming (Course Materials)
- Statistics
- Past Courses
- Introduction to Computational Data Science
- Finite Mathematics
- Differential Equations
- Graph Theory
- Calculus II
- Calculus I
- Multivariable Calculus
- Multivariable Calculus and Modeling
- Complex Analysis
- Calculus I and Modeling
- Linear Algebra
- Network Ranking (Indep. Study)
- Vector Calculus
- Basic College Mathematics
- Calculus for Business Administration and Social Sciences
- Basic Concepts of Elementary Mathematics I
- Precalculus
- Algebraic Structures I
- Analysis I
Recent Ratings
(Year '24/'25, n=85 responding)