Scholar
Elham Afzali
Google Scholar ID: wmms7RwAAAAJ
PhD Candidate in Statistics, University of Manitoba
Citations & Impact
Citations
10
H-index
2
i10-index
0
Publications
5
Co-authors
2
list available
Publications
Resume (English only)
Academic Achievements
- - Publications:
- 1. Navigating interpretability and alpha control in GF-KCSD testing with measurement error: A Kernel approach, 2024
- 2. Gradient-Free Kernel Conditional Stein Discrepancy goodness of fit testing, 2023
- 3. Hybrid VAR-LSTM Networks Modeling and Forecasting COVID-19 Data in Canada, 2021
- - Manuscripts Under Review: Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy, 2025
Research Experience
- - During her PhD at the University of Manitoba, she was involved in multiple research projects, including developing kernel-based methods and Bayesian frameworks to address challenges in high-dimensional, multimodal, and noisy data environments
- - Led the development and implementation of statistical and machine learning solutions in various fields such as neuroscience, genomics, biomedical research, time-series forecasting, and archeology
Education
- - PhD: Statistics, University of Manitoba, January 2020 - August 2025, Supervisors: Dr. Liqun Wang and Dr. Saman Muthukumarana, Thesis: Advanced Kernel-Based Approaches for Robust Inference in Intractable Models
- - M.Sc.: Mathematical Statistics, University of Tehran, September 2014 - September 2016, Thesis: EBSeq-HMM: A Bayesian Approach for Identifying Gene-expression Changes in Ordered RNA-Seq Experiments
- - B.Sc.: Statistics, Allameh Tabataba'i University, September 2009 - September 2013, Dissertation: Medical Image Processing for Early Detection of Alzheimer’s Disease Using Structural MRI
Background
- - Research Interests: Statistical Machine Learning, Kernel & Stein Methods, Bayesian Inference, Gradient-Free Diagnostics, High-Dimensional & Multimodal Data, Computational Statistics, Robust & Interpretable Modelling, Applied Data Science
- - Professional Field: Statistical theory, computational methods, machine learning
- - Introduction: With a strong foundation in statistical theory, computational methods, and machine learning, Elham focuses on developing robust and interpretable statistical methods for intractable models, particularly in the context of Bayesian inference, Stein discrepancies, and kernel-based model evaluation. She has led the development and implementation of statistical and machine learning solutions across diverse domains, including neuroscience, genomics, biomedical research, time-series forecasting, and archeology.
Miscellany
- - Personal Interests: Combining rigorous mathematics with modern computing to deliver reliable and interpretable solutions for complex real-world data
