جست‌وجوی استاد و پژوهشگر

Mohammad Ghasempourمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Causal Inference
• Health Inequalities
• +۳ مورد دیگر

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.

• Causal Inference
• Latent Class Analysis
• Statistical Methods
• +۳ مورد دیگر

Dr. Felix Clouth is an Assistant Professor in the Department of Methodology and Statistics at Tilburg University's Tilburg School of Social and Behavioral Sciences. He completed his doctoral thesis titled 'Latent class models for causal inference' in March 2024, supervised by Professors Jeroen Vermunt and Steffen Pauws. His academic profile shows active research output with publications spanning from 2019 to 2025, demonstrating consistent scholarly activity in statistical methodology. Dr. Clouth's research interests focus on the intersection of causal inference and latent variable modeling. His work centers on developing advanced statistical methods that enable valid causal conclusions from observational data when outcomes or exposures are unobservable constructs. Key areas include: Latent class analysis for causal inference Latent Markov models with the parametric G-Formula Inverse propensity weighting techniques Bias-adjusted three-step methods Applications to patient-reported outcome measures Cancer treatment decision support systems His recent publications show a clear trajectory of methodological innovation with practical applications, particularly in healthcare domains. The 2025 publication on causal inference for latent Markov models represents a significant extension of his work to longitudinal settings, while his 2024 cancer treatment paper demonstrates successful translation of methodological advances into healthcare applications. Dr. Clouth's scholarly impact is reflected in his growing publication record across high-quality journals in methodology and social sciences. His work contributes to UN Sustainable Development Goals through applications in healthcare decision-making and social science research. In terms of teaching, Dr. Clouth offers courses in programming, causal analysis techniques, and statistical methods, reflecting his expertise in both methodological development and practical implementation. His 'You R welcome: programming in R' course suggests a focus on making advanced statistical methods accessible to students and researchers. His collaborative network includes researchers across Tilburg University departments and external partners, as evidenced by his interdisciplinary work on cancer treatment decision support systems. The RepliSims project on simulation study replicability indicates engagement with broader methodological challenges in statistical research.