Yair Goldberg obtained his Ph.D. in Statistics from the Hebrew University of Jerusalem in 2009. From 2009 to 2011, Yair conducted postdoctoral studies at the Department of Biostatistics and served as a statistical consultant for The Translational and Clinical Science Institute (NCTracs), both at the University of North Carolina at Chapel Hill. From 2011 to 2018, Yair worked and taught at the Department of Statistics at the University of Haifa. Yair joined the Faculty of Industrial Engineering and Management in the Technion in 2018.

Selected Publications

1. Goldberg, Y., Zakai, A., Kushnir, D. and Ritov, Y., (2008) “Manifold learning: The price of normalization”. Journal of Machine Learning Research, Vol. 9, pp. 1909–1939.

2. Goldberg, Y. and Ritov, Y., (2009) “Local Procrustes for manifold embedding: a measure of embedding quality and embedding algorithms”. Machine Learning, Vol. 77, pp. 1–25.

3. Goldberg, Y. and Kosorok, M.R., (2012) “Q-Learning with censored data”. Annals of Statistics, Vol. 40, pp. 529–560.

4. Lu, W., Goldberg, Y., and Fine, J., (2012) “On the robustness of adaptive Lasso to model misspecification”. Biometrika, Vol. 99, pp. 717–731.

5. Goldberg, Y., Ritov, Y. and Mandelbaum, A., (2014) “Predicting the continuation of a function with applications to call center data”. Journal of Statistical Planning and Inference, Vol. 147, pp. 53—65.

6. Goldberg, Y., Lu, W. and Fine, J. (2016) “Oracle estimation of parametric transformation models”. Electronic Journal of Statistics, Vol. 10, pp. 90—120.

7. Goldberg, Y. and Kosorok, M. R. (2017) “Support vector regression for right censored data”. Electronic Journal of Statistics, Vol. 11, pp. 532—569.

8. Gorfine, M., Goldberg, Y. and Ritov, Y. (2017) “A quantile regression model for failure-time data with time-dependent covariates”. Biostatistics, Vol. 18, pp. 132—146.

9. Samara, M., Goldberg, Y. (joint first author), Levine, S. Z., Furukawa, T. A., Geddes, J. R., Cipriani, A., Davis, J. M., and Leucht, S. “Initial severity of Bipolar I disorder associated with antipsychotic efficacy: Individual participant data meta-analysis of five placebo-controlled studies". Lancet Psychiatry. Vol. 4, pp. 859—867.

10. Goldberg, Y., Pollak, M., Mitelpunkt, A., Orlovsky, M., Weiss-Meilik, A., and Gorfine, M. (2017) “Change-point detection for infinite horizon dynamic treatment regimes”. Statistical Methods in Medical Research. Vol. 26, pp. 1590—1604.

11. Dasgupta, S., Goldberg, Y. and Kosorok, M. R. “Feature elimination in empirical risk minimization and support vector machines”. The Annals of Statistics.


My current research focuses on machine learning and survival analysis. I am working in both of these fields, and at the interface between them. In the field of survival analysis, I am working on the development and analysis of new models, in both semiparametric and nonparametric frameworks. In the field of machine learning I am currently working on developing fast algorithms for dimension reduction. At the interface between the fields of survival analysis and machine learning, I am working on the development of novel machine learning tools for incomplete data. These tools are applicable to the field of medicine, for example in the development of personalized medicine, where data set are typically both of high dimension and incomplete. In addition, I am working as a biostatistician on several applied projects.

Survival analysis, risk prediction models, empirical processes, machine learning, semiparametric models.

Contact Info

Room 521 Bloomfield Building