Contact
Associate Professor, Faculty of Science - Mathematics & Statistical Sciences
- kashlak@ualberta.ca
Overview
About
PhD, University of Cambridge (2017)
MSc, Johns Hopkins University (2011)
BSc, McGill University (2008)
Research
Nonasymptotic Statistics, High Dimensional & Functional Data, Concentration Inequalities, Stochastic processes, Nonparametric Statistics, Mathematical Statistics
Teaching
Stat 378/502: Applied Regression Analysis
Stat 568: Design and Analysis of Experiments
Courses
STAT 378 - Applied Regression Analysis
Simple linear regression analysis, inference on regression parameters, residual analysis, prediction intervals, weighted least squares. Multiple regression analysis, inference about regression parameters, multicollinearity and its effects, indicator variables, selection of independent variables. Non-linear regression. Prerequisite: One of STAT 266 or STAT 276, or STAT 235 with consent of the Department.
STAT 413 - Computing for Data Science
Survey of contemporary languages/environments suitable for algorithms of Statistics and Data Science. Introduction to Monte Carlo methods, random number generation and numerical integration in statistical context and optimization for both smooth and constrained alternatives, tailored to specific applications in statistics and machine learning. Prerequisites: One of STAT 265 or STAT 281, or consent of the Department.