معرفی
Zelalem Negeri is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the Faculty of Mathematics. His work focuses on statistical methods for diagnostic test accuracy, depression screening tools, and meta-analytic techniques. He collaborates with groups like DEPRESSD (Depression Screening Data) to improve mental health assessment methodologies.
Research interests include robust meta-analysis models for accommodating outliers, optimal cutoff determination for screening tools (e.g., PHQ-9, EPDS), and evaluating diagnostic accuracy across diverse populations. His studies often use individual participant data (IPD) to enhance precision in estimating depression prevalence and screening tool performance.
Recent work emphasizes validating shortened screening tools (e.g., EPDS-Dep-5) and comparing depression assessment methods across modes (e.g., self-administered vs. clinician-administered). He also investigates pandemic-related mental health impacts in populations with systemic sclerosis and explores statistical methods to mitigate biases in data-driven cutoff selection.
No scientific awards are explicitly mentioned. His contributions span collaborations with interdisciplinary teams, including the Scleroderma Patient-centered Intervention Network (SPIN), and involve developing robust statistical frameworks to address variability in diagnostic studies.
