I received my Ph.D. degree in Statistics from the Hebrew University of Jerusalem in 2016 and joined the Department of Statistics at The University of Haifa in 2019. Prior to that I was a postdoctoral researcher at the Department of Biostatistics, Harvard School of Public Health.

Survival analysis and multi-state models for time-to-event data; Selection bias problems, outcome-dependent sampling, random and dependent truncation in time-to-event data, missing data; Causal inference; Federated learning from distributed and heterogeneous data

My research focuses on development of statistical methods for analysis of complex and possibly incomplete data, methods that aim to correct for biased sampling, account for missingness and, in certain cases, allow drawing causal conclusions. 

• B. Vakulenko-Lagun, M. Mandel, R.A. Betensky (2019). Inverse probability weighting methods for Cox regression with right‐truncated data. Biometrics, DOI: 10.1111/biom.13162
• B. Vakulenko-Lagun, J. Qian, S.-H. Chiou, R.A. Betensky (2019). Nonidentifiability in the presence of factorization for truncated data. Biometrika, 106, 3, pp.724-731
• B. Vakulenko-Lagun, M. Mandel, Y. Goldberg (2017). Nonparametric estimation in the illness-death model using prevalent data. Lifetime data analysis, 23 (1), 25-56
• S.D. Oman, B. Vakulenko-Lagun (2009). Estimation of sill matrices in the linear model of coregionalization. Mathematical Geosciences, 41 (1), 15-27