Teaching Philosophy

I start with examples. Always. Not because the formal definition is unimportant, but because an abstract definition with no referent is just notation. Students can memorize it, repeat it on an exam, and still have no idea what they actually learned. The example is what gives the definition somewhere to live.

This applies at every level I’ve taught — from first-year calculus to graduate data science. Before I introduce a concept, I try to put students in a position where they feel the need for it. What problem does this tool solve? What were we unable to do before it existed? Once that question is alive in the room, the definition stops being arbitrary and starts being inevitable.

The second thing I try to do is connect the material to where it actually shows up. This is harder than it sounds. It’s easy to write “applications” on a slide and show a textbook example. It’s much harder to help students feel that what they’re learning is a genuine tool for understanding something real. My time in industry — working on ads systems, fraud detection, customer analytics — gave me a clearer picture of what actually matters in practice, and I try to bring that into the classroom wherever I can. Not to make courses vocational, but to give students a reason to care.

The tension I find hardest to navigate, especially in data science, is depth versus breadth. The field touches everything: statistics, optimization, software engineering, domain knowledge. A course could expand infinitely and still have gaps. My resolution is to go broad deliberately. Depth is something students can pursue on their own — and with AI tools available today, going deep on any topic has never been more accessible. What’s harder to acquire alone is the map: knowing what exists, what connects to what, and where to look when you need something. That’s what a course can give that self-study struggles to replicate. I pair that breadth with my own experience in industry, so students don’t just see the landscape abstractly but understand where each piece actually gets used.

Lecturer

University of Toronto

MMF1922H (Data Science)
2020 Fall, 2021 Fall, 2022 Fall, 2023 Fall, 2024 Fall, 2025 Fall

MAT133Y (Calculus and Linear Algebra for Commerce)
2016 Summer

Teaching Assistant

University of Toronto

MAT237Y (Multivariable Calculus)
2013 Summer, 2014 Summer

MAT244H (Introduction to Ordinary Differential Equations)
2012 Summer

APM466H/MAT1856H (Mathematical Theory of Finance)
2015 Fall, 2016 Fall

University of Toronto Mississauga

ECO200Y (Microeconomic Theory)
2009 Summer

ECO220Y (Quantitative Methods in Economics)
2008 Fall, 2009 Fall

MAT133Y (Calculus and Linear Algebra for Commerce)
2008 Fall, 2009 Fall, 2010 Summer, 2010 Fall, 2014 Summer

MAT134Y (Calculus for Life Sciences)
2009 Fall, 2010 Fall, 2011 Fall, 2012 Fall, 2013 Fall

MAT135Y (Calculus)
2009 Fall

MAT137Y (Calculus)
2010 Fall

MAT202H (Introduction to Discrete Mathematics)
2014 Fall

MAT212H (Modeling with Differential Equations in Life Sciences and Medicine)
2012 Winter

MAT223H (Linear Algebra I)
2010 Summer

MAT233H (Calculus of Several Variables)
2011 Fall, 2014 Fall

MAT311H (Partial Differential Equations)
2013 Fall

MAT378H (Introduction to Analysis)
2013 Winter, 2014 Winter, 2015 Winter

MAT402H (Classical Geometries)
2013 Fall

MAT406H (Mathematical Introduction to Game Theory)
2012 Fall, 2014 Winter, 2014 Fall

PHL245H (Modern Symbolic Logic)
2008 Fall

STA107H (An Introduction to Probability and Modelling)
2012 Winter, 2012 Fall

STA219H (Mathematics of Investment and Credit)
2012 Fall

STA256H (Probability and Statistics I)
2013 Fall, 2014 Fall, 2014 Summer

STA257H (Probability and Statistics I)
2011 Fall, 2012 Fall

STA258H (Statistics with Applied Probability)
2014 Winter

STA260H (Probability and Statistics II)
2014 Winter

STA261H (Probability and Statistics II)
2012 Winter

STA302H (Regression Analysis)
2014 Fall

STA313H (Topics in Statistics: Applications of Statistical Models - Measure Theory)
2014 Winter

STA348H (Introduction to Stochastic Processes)
2012 Fall, 2014 Fall

STA313H (Topics in Statistics: Applications of Statistical Models)
2014 Winter

STA413H (Estimation and Testing)
2013 Fall

STA431H (Structural Equation Models)
2014 Fall

STA437H (Applied Multivariate Statistics)
2012 Fall

University of Toronto Scarborough

MATA23H (Linear Algebra I)
2012 Summer, 2014 Winter

MATA33H (Calculus for Management II)
2013 Summer

MATC34H (Complex Variables)
2013 Fall

MATC46H (Differential Equations II)
2014 Winter

MGEA02H (Introduction to Microeconomics: A Mathematical Approach)
2013 Summer

MGEB02H (Price Theory: A Mathematical Approach)
2013 Summer

MGEC72H (Financial Economics)
2013 Summer

MGFB10H (Principles of Finance)
2013 Summer