Mr. Milad Jamali-dolatabad, Tabriz University of Medical Sciences, Iran
Milad Jamali-Dolatabad is a biostatistics expert and researcher specializing in traffic injury prevention and data analysis. He earned his Master’s degree in Biostatistics from Tabriz University of Medical Sciences, Iran, focusing on applying advanced statistical methods to traffic data (šš). His research, which includes publications on pedestrian accident outcomes and road traffic mortality, has been featured in renowned journals like BMC Public Health and Traffic Injury Prevention (šš¶āāļø). Proficient in R, Stata, and SPSS, Milad is involved in several projects that analyze traffic accident data and model road traffic mortality trends (š»š). He is fluent in Turkish and Persian, with intermediate English proficiency (š£ļøš).
Publication profile
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Academic Background š
Milad Jamali-Dolatabad holds a Master’s degree in Biostatistics from Tabriz University of Medical Sciences, Iran, earned between 2016 and 2020. His thesis, supervised by Dr. Parvin Sarbakhsh, focused on the application of Partial Least Squares (PLS) methods to analyze traffic data, comparing their efficacy with traditional approaches. He achieved an impressive dissertation grade of 18.89 out of 20, reflecting his academic excellence (š).
Professional Experience š¼
Milad has been instrumental in various research projects. Notably, he contributed to the development of frameworks for synchronized data mining using driving simulators and EEG data in traffic laboratories. His expertise extends to modeling road traffic mortality dynamics and identifying hidden patterns in pedestrian characteristics using statistical methodologies.
Research Focus
Milad Jamali-Dolatabad’s research primarily focuses on traffic injury prevention and epidemiology, leveraging advanced statistical methodologies. His studies often involve analyzing predictors of fatal outcomes in pedestrian accidents and road traffic injuries in Iranian populations. Using techniques like Partial Least Squares (PLS) and categorical principal component analysis (CATPCA), he explores hidden patterns and trends in mortality data, particularly related to pedestrian safety and traffic accident dynamics (š¶āāļøš). His work contributes significantly to understanding factors influencing road traffic mortality and developing strategies for safer transportation systems, reflecting a commitment to improving public health through rigorous statistical analysis and evidence-based research.
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