Kashlak

Dr. Kashlak is an Associate Professor in the Department of Mathematical & Statistical Sciences at the University of Alberta. He holds a PhD from the University of Cambridge (2017), MSc from Johns Hopkins University (2011), and BSc from McGill University (2008). His research focuses on nonasymptotic statistics, concentration inequalities, functional data analysis, and probabilistic methods in Banach spaces. He is particularly known for developing permutation and randomization tests, integrating group theory into statistical frameworks, and applying statistical methods to diverse datasets ranging from sleep apnea diagnosis to electoral analysis.

**Research Contributions**: His work bridges theoretical statistics and applied problems, with key contributions in covariance operator inference, topological hidden Markov models, and sparse precision matrix estimation. Notable publications include advancements in bootstrap methods for generalized linear models and analytic permutation testing for functional ANOVA. He has collaborated with institutions globally, including the University of Cambridge and Baylor University.

**Teaching & Outreach**: He teaches advanced courses like Applied Regression Analysis (Stat 378) and Probability and Measure (Stat 571). His YouTube channels, 'Cache Lack Stats' and 'Cache Lack Math & Stats Lectures', provide accessible explanations of statistical concepts and dataset analyses. He has developed R packages like sparseMatEst for sparse covariance estimation and fdcov for functional data analysis.

**Awards & Service**: Kashlak has received an NSERC Discovery Grant ($161,000), SSC New Investigator Award, and multiple grants from the University of Alberta. He organized workshops like the Alberta Math Dialogue (2019) and served as Session Chair at EcoSta 2019 and WNAR 2018. His work has been featured in Significance Magazine and presented at global conferences such as the IMS-Bernoulli Symposium and the RSS Conference.

**Lab & Collaborations**: His research group focuses on statistical methodology with applications in healthcare, spatial analysis, and high-dimensional data. He actively mentors students, including Xinyu Zhang, and collaborates with researchers in machine learning and biomedical fields.