معرفی
Mohammad Ghasempour is a Research Fellow at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, specializing in the Department of Statistics. His work focuses on advancing machine learning techniques for causal inference and addressing health inequalities through statistical methodologies.
Education & Affiliations:
- Postdoctoral Fellow at USBE, Umeå University
Research Interests:
His research emphasizes integrating machine learning with causal inference to tackle complex problems in health disparities and statistical efficiency. He explores applications such as convolutional neural networks for causal analysis and developing R packages for causal dimension reduction. His work bridges computational methods and real-world challenges in public health and labor market studies.
Research Projects:
- Machine learning to study causality with big datasets (2022–2026): Focuses on valid statistical conclusions via scalable methods.
- Statistical models for life trajectories (2017–2021): Analyzes labor market and health dynamics using advanced statistical frameworks.
Grants & Collaborations: Member of the Stat4Reg research group, contributing to interdisciplinary projects at the intersection of statistics and societal challenges.
Labs/Teams: Active in the Stat4Reg group, advancing statistical methodologies for regulatory and applied research.
