Teaching

I am a mathematics professor with experience teaching college mathematics, applied data science, machine learning, AP mathematics, and online technical courses. My teaching emphasizes clarity, accessibility, kindness, structured practice, and helping students develop confidence with challenging quantitative material.

Teaching Areas

College Mathematics

  • College Algebra
  • Calculus I
  • Calculus II
  • Discrete Mathematics
  • Introduction to Differential Equations

Statistics, Data Science, and AI

  • Data Science
  • Machine Learning Foundations
  • Python for Data Science
  • Exploratory Data Analysis
  • Machine Learning

Advanced High School and International Instruction

  • AP Calculus AB
  • AP Calculus BC
  • AP Statistics

Curriculum and Course Development

  • Designed and taught machine learning foundations material
  • Developed data science and machine learning projects
  • Created mathematics assessments, solution sets, and instructional materials
  • Supported AI and data science curriculum development

Rose State College Courses

MATH 1513 College Algebra

Institution: Rose State College

Terms: Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026

Taught in both in-person and online formats. Course work emphasizes algebraic reasoning, functions, equations, graphing, modeling, and preparation for future quantitative coursework.

MATH 1914 Differential and Integral Calculus I

Institution: Rose State College

Terms: Fall 2023, Spring 2024, Summer 2024, Fall 2024

Taught limits, derivatives, applications of differentiation, integrals, and foundational concepts for continued study in calculus and applied mathematics.

MATH 2924 Calculus II

Institution: Rose State College

Terms: Summer 2024, Fall 2024, Summer 2025, Fall 2025, Spring 2026

Taught integration techniques, applications of integration, sequences, series, convergence, and related topics requiring sustained symbolic reasoning and structured problem solving.

MATH 2103 Discrete Mathematics

Institution: Rose State College

Terms: Fall 2023, Fall 2024, Spring 2025, Summer 2025, Fall 2025, Spring 2026

Taught logic, sets, functions, relations, proof methods, counting, algorithms, number theory, recurrence, graph theory, and foundational mathematical reasoning.

MATH 2973 Introduction to Differential Equations

Institution: Rose State College

Terms: Spring 2024, Spring 2025, Spring 2026

Taught first-order equations, second-order linear equations, Laplace transforms, using matrices to solve systems, modeling applications, and qualitative interpretation of differential equation behavior.

MATH 2213 Data Science

Institution: Rose State College

Terms: Spring 2024, Spring 2025

Taught introductory data science concepts, including data cleaning, exploratory analysis, visualization, building and evaluating models, communicating results and deployment.

AIML 1013 Machine Learning Foundations

Institution: Rose State College

Terms: Fall 2025

Taught foundational machine learning concepts with emphasis on applied modeling, data preparation, model interpretation, and creating end to end ml applications that are deployed.

Additional Teaching and Training Experience

AP Calculus AB, AP Calculus BC, and AP Statistics

Institution: American Straight A Academy

Taught accelerated AP mathematics and statistics courses remotely to international students, emphasizing conceptual clarity, exam preparation, and structured problem solving.

Python for Data Science, Exploratory Data Analysis, and Machine Learning

Institution: Birchwood University

Taught applied data science and machine learning topics including Python workflows, exploratory analysis, modeling, and practical implementation.

Teaching Philosophy

My teaching philosophy is grounded in a human-centered, kindness-first approach. I believe students learn best when they feel respected, supported, and capable of growth. Mathematics, statistics, data science, and machine learning can be intimidating subjects, so I try to make difficult ideas accessible without removing the rigor.

I do not believe the purpose of mathematics education is to “weed people out.” I believe in meeting students where they are, identifying the gaps that are preventing progress, and helping them build confidence through clear explanations, structured practice, and consistent feedback.

I value a growth mindset in the classroom. Many students arrive with anxiety, uneven preparation, or past experiences that made them believe they are not “math people.” My role is to help students see that quantitative thinking can be developed through patience, practice, good habits, and persistence.

My approach combines high expectations with high support. I want students to develop technical skill, mathematical maturity, independence, and disciplined problem-solving habits, but I also believe that rigor should be paired with compassion, clarity, and accessibility.

Whether I am teaching college algebra, calculus, discrete mathematics, statistics, data science, or machine learning, my goal is to help students understand both the structure of the ideas and their practical usefulness. I want students to leave my courses with stronger reasoning skills, more confidence, and a greater belief in their ability to learn difficult things.

My teaching work connects directly to my broader interests in quantitative methods, applied statistics, educational data analysis, student success, machine learning, and research-informed decision making.