Willem van den Boom

headshot Willem
Hi! I am a Scientist (Biostatistics) in the Institute for Human Development and Potential at the Agency for Science, Technology and Research (A*STAR) in 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 at the National University of Singapore. Currently, I work with Prof. Maria De Iorio on Bayesian inference for graphical models and am also involved in a variety of other statistical projects ranging from economics to medicine.

Pages

Contact Me

You can contact me via email at willem@wvdboom.nl. Also, feel free to check out my LinkedIn profile.

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

See also my CV and Google Scholar.

  1. Cremaschi, A., van den Boom, W., Ng, N.B.H., Franzolini, B., Tan, K.B., Yen, J.C.K., Tan, K.H., Chong, Y.-S., Eriksson, J.G., De Iorio, M. (2025). Post-partum screening for type 2 diabetes in women with a history of gestational diabetes mellitus: A cost-effectiveness analysis in Singapore. Value in Health Regional Issues, 45, 101048. doi:10.1016/j.vhri.2024.101048
  2. van den Boom, W., De Iorio, M., Qian, F., and Guglielmi, A. (2024). The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis. Biometrics, 80(3), ujae075. doi:10.1093/biomtc/ujae075
  3. van den Boom, W., Cremaschi, A., and Thiery, A. H. (2024). Doubly adaptive importance sampling. doi:10.48550/arXiv.2404.18556
  4. Qian, F., van den Boom, W., and See, K.C. (2024). The new global definition of acute respiratory distress syndrome: Insights from the MIMIC-IV database. Intensive Care Medicine, 50(4), 608–609. doi:10.1007/10.1007/s00134-024-07383-x
  5. Saini, S., Manai, G., van den Boom, W., De Iorio, M., and Qian, F. (2024). Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain. Discover Analytics, 2, 5. doi:10.1007/s44257-024-00013-2
  6. Natarajan, A., van den Boom, W., Odang, K.B., and De Iorio, M. (2024). On a wider class of prior distributions for graphical models. Journal of Applied Probability, 61(1), 230–243. doi:10.1017/jpr.2023.33
  7. Feng, S.F., van den Boom, W., De Iorio, M., Thng, G.J., Chan, J.K.Y., Chen, H.Y., Tan, K.H., and Kee, M.Z.L. (2024). Joint modelling of mental health markers through pregnancy: A Bayesian semi-parametric approach. Journal of Applied Statistics, 51(2), 388–405. doi:10.1080/02664763.2022.2154329
  8. van den Boom, W., De Iorio, M., and Beskos, A. (2023). Bayesian learning of graph substructures. Bayesian Analysis, 18(4), 1311–1339. doi:10.1214/22-BA1338
  9. De Iorio, M., van den Boom, W., Beskos, A., Jasra, A., and Cremaschi, A. (2023). Graph of graphs: From nodes to supernodes in graphical models. doi:10.48550/arXiv.2310.11741
  10. Qian, F., van den Boom, W., and See, K.C. (2023). Real-world evidence challenges controlled hypoxemia guidelines for critically ill patients with chronic obstructive pulmonary disease. Intensive Care Medicine, 49(9), 1133–1135. doi:10.1007/s00134-023-07166-w
  11. Young, A.L., van den Boom, W., Schroeder, R.A., Krishnamoorthy, V., Raghunathan, K., Wu, H.T., and Dunson, D.B. (2023). Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data. PLOS ONE, 18(4), e0284904. doi:10.1371/journal.pone.0284904
  12. Franzolini, B., Cremaschi, A., van den Boom, W., and De Iorio, M. (2023). Bayesian clustering of multiple zero-inflated outcomes. Philosophical Transactions of the Royal Society A, 381(2247), 20220145. doi:10.1098/rsta.2022.0145
  13. 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, 31(4), 1215–1224. doi:10.1080/10618600.2022.2050250 pdf
  14. 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(3), 36. doi:10.1007/s11222-022-10093-3 pdf
  15. 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. doi:10.1177/09622802211048059
  16. 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. doi:10.3390/jpm11090921
  17. 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. doi:10.1093/biomet/asaa068 pdf
  18. 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. doi:10.1016/j.chest.2019.09.015
  19. 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
  20. 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
  21. 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
  22. van den Boom, W., Reeves, G. and Dunson, D.B. (2015). Scalable approximations of marginal posteriors in variable selection. doi:10.48550/arXiv.1506.06629

Teaching

Experience

In 2019 and 2020, I taught Introduction to Data Science for three semesters, a course which I designed and introduced at Yale-NUS College. Additionally, I taught Quantitative Reasoning at Yale-NUS.

At Duke in Summer 2017, I taught STA 104 Data Analysis and Statistical Inference, the first course for which I bore full responsibility as Instructor of Record. In Fall 2016, I was head teaching assistant (TA) for STA 101 Data Analysis and Statistical Inference, a course with twelve TAs in total. I was TA for STA 601 Bayesian Methods and Modern Statistics in Fall 2017. Amongst others, I led weekly lab sessions as TA.

During my undergraduate degree at University College Roosevelt, I was a TA for Mathematical Ideas & Methods in Context, leading a weekly group session.

Since high school, I have been an avid tutor eager to help (fellow) students, appreciating teaching one on one. This is not restricted to Statistics and Mathematics. I for instance volunteered as a euphonium specialist to support primary school students at Kidznotes, a local music program for social change, during my PhD.

Teaching Statement

My teaching statement is available upon request.