Teaching

How I Think About It

Teaching Philosophy

I don’t think of teaching and research as separate activities: explaining a method well is often what reveals whether I actually understand it. A few convictions I try not to compromise on:

Understanding before implementation. I would rather a student spend an extra session understanding why a method works than memorize the steps to use it. Syntax and formulas fade; reasoning transfers.

Curiosity is the actual prerequisite. Not talent, not prior background: the willingness to ask “why does this work” instead of accepting a rule. I try to protect that curiosity rather than train it out of students with rote procedure.

Progressive complexity. Every subject I teach follows the same arc: build intuition first, formalize it second, and only then layer on technical complexity. Starting with the formalism is efficient for me and confusing for almost everyone else.

Critical thinking over correct answers. A student who can explain why a wrong answer is wrong has learned more than one who guessed the right one. I’d rather grade reasoning than outcomes.

How Every Subject Is Taught

Learning Framework

Every subject I teach follows the same progression, regardless of topic:

1. Understand
2. Model
3. Implement
4. Experiment
5. Interpret
6. Communicate

The goal at each stage is to build intuition before technical complexity, not to skip the complexity, but to earn it.

What I Teach

Subjects I Teach

Grouped into coherent units rather than isolated topics, each grounded in real teaching or professional experience.

• Applied Mathematics & Calculus

  Logic, algebra, calculus, and discrete structures, taught with a practical bridge to computing and data science rather than as abstract exercises.

  Foundational · Undergraduate students, career changers

  Applications: Software engineering, data science, computational thinking

• Linear Algebra & Probability

  The two pillars underneath most of modern data science and machine learning, built from geometric and probabilistic intuition rather than memorized formulas.

  Foundational to Intermediate · Undergraduate and Master's students

  Applications: Machine learning, statistics, numerical methods

• Statistics & Numerical Analysis

  Statistical inference and the numerical methods that make mathematical models computable, with an emphasis on stability and honest uncertainty.

  Intermediate · Master's students, researchers

  Applications: Data analysis, scientific computing, simulation

• Scientific Computing

  Translating mathematical models into working numerical code: finite differences, stability, and the gap between a correct equation and a correct simulation.

  Intermediate to Advanced · Master's students, PhD applicants, researchers

  Applications: Simulation, applied research, computational modelling

• Python for Data Science

  Data wrangling, exploratory analysis, and model building with Pandas, NumPy, and Scikit-learn, grounded in real workflows rather than isolated syntax.

  Foundational to Intermediate · Career changers, professionals, undergraduate students

  Applications: Data analysis, machine learning pipelines, automation

• SQL, Data Analysis & Business Intelligence

  Querying, reporting, and dashboarding with SQL and Power BI, drawn directly from real analytics and reporting work rather than textbook examples.

  Foundational to Intermediate · Professionals, career changers

  Applications: Reporting, dashboards, business analytics

• Machine Learning & Artificial Intelligence

  Classical machine learning and applied AI, taught with an emphasis on interpretability and validation over treating models as black boxes.

  Intermediate to Advanced · Master's students, professionals, researchers

  Applications: Predictive modelling, decision support, applied AI

• Data Engineering & Analytics Engineering

  Building the pipelines and infrastructure that make analysis reproducible: the less visible discipline underneath every data science result.

  Intermediate to Advanced · Professionals, career changers

  Applications: Data pipelines, analytics platforms, reproducible workflows

Adapted, Not Uniform

Who I Teach

Teaching is adapted to each learner’s objectives and background, not delivered the same way regardless of audience.

• Undergraduate Students
• Master's Students
• PhD Applicants
• Researchers
• Professionals
• Career Changers
• High-School Students

Not a Passive Lecture

Learning Experience

A session is built around active participation:

• Conceptual Explanation

  Building the intuition before the formalism.

• Worked Examples

  Seeing the method applied before applying it yourself.

• Hands-on Implementation

  Writing the code or working the proof, not watching it.

• Exercises

  Deliberate practice on problems chosen to expose gaps, not confirm mastery.

• Discussion

  Explaining your reasoning out loud, where the actual gaps in understanding surface.

• Review

  Revisiting what didn't stick the first time, honestly.

• Real-World Applications

  Connecting the method back to a problem worth solving.

Beyond the Session

Educational Resources

Materials I develop and share to support learning beyond the session itself, an ongoing collection, not a fixed set:

• Lecture Notes
• Programming Notebooks
• Interactive Demonstrations
• Slides
• GitHub Repositories
• Scientific Articles
• Tutorials
• Exercises
• Reading Recommendations
• Video Lectures (Planned)

Beyond the Classroom

Mentoring

Mentoring is where teaching gets personal: less about a subject, more about a specific person’s next step. The focus is always on building scientific autonomy: helping someone reach the point where they no longer need me to check their reasoning.

• Research Methodology
• Scientific Writing
• Data Science Projects
• Programming
• Mathematical Modelling
• Machine Learning
• Career Guidance
• Graduate School Preparation
• Research Internships

A Cycle, Not a Sequence

Teaching & Research

Research generates new knowledge. Projects transform ideas into practical solutions. Teaching makes both accessible, and teaching a concept well routinely surfaces the next question worth researching.

1. Research
2. Projects
3. Teaching
4. New Questions

The Concrete Record

Where I’ve Taught

In Their Words

Testimonials