Willem van den Boom

I am a Senior Research Fellow at the Yong Loo Lin School of Medicine of the National University of Singapore.

I got my BSc with a focus on Mathematics and Computer Science in 2014 from University College Roosevelt, a small liberal arts college in the Netherlands, and my PhD in Statistics from Duke University in 2018 under supervision of David Dunson and Galen Reeves. I moved to Singapore in 2018 for a postdoctoral Research Fellowship with Alexandre Thiery. Currently, I work with Prof. Maria De Iorio on Bayesian inference for graphical models.

Research

My research interests are in Bayesian Statistics and more specifically applications, scalable computation and related theory, and Gaussian graphical models. Additionally, I try to advance clinical knowledge by working on medical records data in close collaboration with clinicians.

Publications

• Franzolini, B., Cremaschi, A., van den Boom, W., and De Iorio, M. (2022). Bayesian clustering of multiple zero-inflated outcomes.
• Natarajan, A., van den Boom, W., Odang, K.B., and De Iorio, M. (2022). On a wider class of prior distributions for graphical models.
• van den Boom, W., Jasra, A., De Iorio, M., Beskos, A., and Eriksson, J.G. (2022). Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo. Statistics and Computing, 32, 36.
• van den Boom, W., De Iorio, M., and Beskos, A. (2022). Bayesian learning of graph substructures.
• van den Boom, W., Beskos, A., and De Iorio, M. (2022). The G-Wishart weighted proposal algorithm: Efficient posterior computation for Gaussian graphical models. Journal of Computational and Graphical Statistics, advance online publication.
• van den Boom, W., De Iorio, M., and Tallarita, M. (2022). Bayesian inference on the number of recurrent events: A joint model of recurrence and survival. Statistical Methods in Medical Research, 31(1), 139–153.
• Lysaght, T., Ballantyne, A., Toh, H.J., Lau, A., Ong, S., Schaefer, O., Shiraishi, M., van den Boom, W., Xafis, V., and Tai, E.S. (2021). Trust and trade-offs in sharing data for precision medicine: A national survey of Singapore. Journal of Personalized Medicine, 11(9), 921.
• van den Boom, W., Reeves, G., and Dunson, D.B. (2021). Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation. Biometrika, 108(2), 269–282.
• van den Boom, W., Hoy, M., Sankaran, J., Liu, M., Chahed, H., Feng, M., and See, K.C. (2020). The search for optimal oxygen saturation targets in critically ill patients: Observational data from large ICU databases. Chest, 157(3), 566–573.
• 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.
• 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.
• 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.
• van den Boom, W., Reeves, G. and Dunson, D.B. (2015). Scalable approximations of marginal posteriors in variable selection.