My research interests are in Bayesian Statistics and more specifically applications, scalable computation and related theory, and functional data analysis. Additionally, I try to advance clinical knowledge by working on medical records data in close collaboration with clinicians. Through my collaboration with Galen Reeves, I am familiar with Information Theory and look at how it can be applied to or rather inspire statistical inference and theory.
van den Boom, W., Hoy, M., Sankaran, J., Liu, M., Chahed, H., Feng, M., and See, K.C. (2019). The search for optimal oxygen saturation targets in critically ill patients: Observational data from large ICU databases. CHEST in press. doi:10.1016/j.chest.2019.09.015
van den Boom, W., Reeves, G., and Dunson, D.B. (2019). Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation. arXiv:1909.06753
van den Boom, W., Mao, C., Schroeder, R.A., and Dunson, D.B. (2018). Extrema-weighted feature extraction for functional data. Bioinformatics, 34(14), 2457–2464. doi:10.1093/bioinformatics/bty120
van den Boom, W., Schroeder, R.A., Manning, M.W., Setji, T.L., Fiestan, G., and Dunson, D.B. (2018). Effect of A1C and glucose on postoperative mortality in noncardiac and cardiac surgeries. Diabetes Care, 41(4), 782–788. doi:10.2337/dc17-2232
van den Boom, W., Dunson, D., and Reeves, G. (2015). Quantifying uncertainty in variable selection with arbitrary matrices. IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 385–388. doi:10.1109/CAMSAP.2015.7383817
van den Boom, W., Reeves, G. and Dunson, D.B. (2015). Scalable approximations of marginal posteriors in variable selection. arXiv:1506.06629