Innovative Research Award
Samaneh Eftekhari Mahabadi
University of Tehran, Iran
Samaneh Eftekhari Mahabadi is a researcher affiliated with the University of Tehran whose reported scholarly record includes work in statistical modelling, missing-data methodology, machine learning, longitudinal inference, and applications involving clinical and financial data. The documented publication portfolio provides a basis for academic recognition in data science and analytics.
Abstract
The research record associated with Samaneh Eftekhari Mahabadi spans statistical computation, machine learning, missing-data imputation, competing-risks analysis, and Bayesian sensitivity analysis. Her publications address methodological questions that arise when incomplete, masked, or non-ignorable data affect statistical inference. These topics are relevant to modern data science because reliable analysis increasingly depends on methods capable of accounting for uncertainty and incomplete observations. [1]
Keywords
Data science; statistical modelling; machine learning; missing data; multiple imputation; Bayesian inference; longitudinal analysis; competing risks; sensitivity analysis; clinical trial data.
Introduction
Contemporary statistical research frequently combines computational techniques with rigorous inferential frameworks. Mahabadi’s documented publications reflect this intersection, particularly through supervised learning approaches to missing-data imputation and machine-learning methods for masked competing-risks data. [2] Such work contributes to methodological discussions concerning the validity and robustness of conclusions derived from incomplete datasets.
Research Profile
The supplied scholarly record reports 47 documents, 238 citations, and an h-index of 8. The stated subject area is Data Science and Analytics. Her research profile is characterized by methodological statistics and applications involving complex datasets, with publications appearing in journals and scholarly book collections. [1]
Research Contributions
- Development and evaluation of approaches for missing-data imputation using supervised learning methods. [4]
- Application of machine-learning algorithms to masked competing-risks data. [2]
- Bayesian second-order sensitivity analysis addressing non-ignorability in longitudinal inference. [1]
- Analysis of monetary policy and Islamic-bank stability under differing governance models. [3]
Publications
- Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics, 2024. [1]
- Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation, 2022. [2]
- The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. 2021. [3]
- Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications, 2021. [4]
Research Impact
The reported citation count and h-index indicate that the publication record has received scholarly citations. More specifically, the documented research addresses recurring challenges in statistical analysis where missingness, masking, competing risks, or non-ignorability can influence inference. The supplied profile reports 238 citations across 47 documents, providing quantitative context for the recognition of this research record.
Award Suitability
For the Innovative Research Award, the documented combination of statistical methodology, machine learning, and applied research provides relevant evidence for consideration. The publication portfolio demonstrates engagement with methodological problems and their applications rather than relying on a single research theme. Final award decisions remain subject to the criteria and review procedures of the Computer Scientists Awards.
Conclusion
Samaneh Eftekhari Mahabadi’s documented research profile reflects sustained scholarly activity in data science and analytics, with particular emphasis on statistical inference, machine learning, and incomplete-data methodology. The cited publications provide a structured basis for evaluating her research contributions in the context of an academic recognition program.
External Links
References
- Eftekhari Mahabadi, S. et al. (2024). Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics.
https://doi.org/10.1515/ijb-2022-0014
- Eftekhari Mahabadi, S. et al. (2022). Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation.
https://doi.org/10.1080/00949655.2022.2063864
- Eftekhari Mahabadi, S. et al. (2021). The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. Monetary Policy, Islamic Finance, and Islamic Corporate Governance.
https://doi.org/10.1108/978-1-80043-786-920211009
- Eftekhari Mahabadi, S. (2021). Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications.
http://jsm.yazd.ac.ir/article_2049.html
- ORCID. (n.d.). Samaneh Eftekhari Mahabadi, ORCID record.
https://orcid.org/0000-0001-8938-5864
- Computer Scientists Awards. (n.d.). Computer Scientists Awards official website.
https://computerscientists.net/