Hammad Shaikh
Assistant Professor at University of Stavanger
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolHammad Shaikh is an Assistant Professor and applied economist with a PhD from the University of Toronto and eight years of experience blending field experiments, causal inference, and data science to improve student outcomes in higher education. His research uses behavioural economics and low-cost, scalable interventions—combined with estimatable effort-choice models—to design more effective online foundational courses. He teaches and mentors extensively, has an award-winning record as a TA and instructor, and brings hands-on implementation skills in Stata, R, Matlab, and Python. Hammad also publishes educational Jupyter notebooks and web projects on GitHub (400+ stars), including from-scratch ML tutorials that demonstrate practical optimization and regularization techniques. He is experienced with experimental methods including adaptive/contextual bandit algorithms and applies these in field and online settings to bridge research and classroom practice. Based in Toronto, he combines rigorous econometric methods with computational work to make evidence-based, actionable improvements in education policy and pedagogy.
8 years of coding experience
Data Science, Data Science at CourseraPhD Economics (in progress), Economics of Education, PhD Economics (in progress), Economics of Education at University of TorontoBachelor's degree - BSc, Economics, Bachelor's degree - BSc, Economics at University of Toronto Mississauga
Github Skills (12)
algorithm10
data-analysis10
jupyter-notebook10
python10
optimizers10
optimisation10
optimization10
linear-regression10
data-visualisation9
data-visualization9
data-visualizations9
regression9
Programming languages (4)
Github contributions (5)
hammadshaikhha/Data-Science-and-Machine-Learning-from-Scratch
Jun 2017 - Nov 2021
Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.
Role in this project:
Data Scientist
Data ScientistContributions:182 commits, 1 PR, 176 pushes in 4 years 4 months
Contributions summary:Hammad implemented and refined a Jupyter Notebook demonstrating Newton's method for optimization, including its application to a dataset of IMDB movie data. The contributions focused on applying the method to model relationships between IMDB scores and movie gross sales, as well as exploring the effects of L1 and L2 regularization methods to this dataset. The user also corrected a typo in the code and added code comments.
pythonsciencedata-sciencemachine-learning-algorithmsmachine-learning
hammadshaikhha/Weight-Food-Excercise-Data
Dec 2019 - Aug 2024
Contributions:12 pushes, 1 branch in 4 years 8 months
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