Teaching

Courses, teaching philosophy, and educational resources

Teaching Philosophy

My teaching philosophy is grounded in the belief that knowledge is meant to be shared and translated into impact. I adopt a student-centered approach, delivering complex material accessibly while building strong foundations in theory and mathematics. My courses are driven by real-world problems, incorporating hands-on coding, open-source data, and applied projects to cultivate practical skills and critical thinking. I strongly emphasize openness and reproducibility, encouraging version control, reproducible workflows, and high-performance computing. Teaching, to me, is a collaborative and evolving process, one where both instructor and students learn together through shared inquiry and exploration.

Teaching Session

Current Courses

Introduction to Statistics (1st year undergrad course) Fall 2025

An introduction to statistical thinking and methodology, covering data visualization, experimental design, probability, and statistical inference. Topics include confidence intervals, significance testing, simulation, and real-world paradoxes such as Simpson’s paradox.

Experimental Design (4th Year undergrad course) Winter 2025

This course explores statistical principles for designing effective experiments, with emphasis on practical applications. Topics include randomized block, factorial, fractional factorial, nested, and Latin square designs.

Survival Analysis (Integrated 4th year undergrad + grad level) Winter 2025

An introduction to statistical methods for analyzing censored time-to-event data, with applications in medicine and industry. Topics include Kaplan-Meier estimation, proportional hazards models, accelerated failure time models, and time-dependent covariates.

Past Courses

Introduction to Machine Learning for Big Data in Healthcare Fall 2020-2022 (Dalla Lana School of Public Health, University of Toronto)

An introductory course covering the fundamentals of ML and big data

Student Supervision

Dr. Sharma supervises graduate and undergraduate students in various research areas related to machine learning, deep learning and healthcare analytics. Please feel free to go through the Prospective Students section for applying for research positions