Adel

Dr. Adel Mohammadpour

PhD

Affiliations

Adjunct Assistant Professor

University of Calgary

Contact information

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Research partners

Funding

Background

Educational Background

BSc, MSc, and PhD in Statistics, Shiraz University,

PostDoc Researcher CentraleSupélec, CNRS, Université Paris-Saclay, Laboratoire des signaux et systèmes (L2S),

Research

Areas of Research

Evaluation of ML models, Statistical learning of heavy-tailed data, Unification of statistical methods, Environmental, biomedical, and industrial applications.

Courses

Course number Course title Semester
DATA 606 Statistical Methods in Data Science Winter 2025
STAT 217 Introduction to Statistics II Spring 2025
STAT 205 Introduction to Statistical Inquiry Spring 2025
DATA 691 Integrated Topics in Data Science and Analytics Fall 2025

Projects

Model Evaluation

Model evaluation is the process of finding the optimal classifier for prediction.

 Is Evaluation Based on Accuracy of Classification Algorithms Misleading? 

An Approach to Model Validation Using Bayes Error Rate.


Machine Learning Algorithms for Non-Gaussian Heavy-Tailed Data

The Generalized Central Limit Theorem states that the limiting distributions of properly normalized sums of independent random vectors are stable without a finite variance assumption. Despite over a century of research there is still no widely accepted strategy for implementing robust parametric methods in machine learning for such data, commonly referred to as heavy-tailed data. This research program aims to develop a new generation of machine learning algorithms tailored to non-Gaussian, heavy-tailed data.