Division of Biomedical Data Science

Research topics

Selected publications

Preprints

Cabral, R., De Iorio, M., Harris, A. (2024). Scalable Bayesian Clustering for Integrative Analysis of Multi-View Data. doi:10.48550/arXiv.2408.17153

Cremaschi, A., Cadonna, A., Guglielmi, A., and Quintana, F. (2023). A change-point random partition model for large spatio-temporal datasets. doi:10.48550/arXiv.2312.12396

Cremaschi, A., De Iorio, M., Page, G., Jasra, A. (2025). Latent Modularity in Multi-View Data. arXiv:2511.00455.

Cremaschi, A., Franzolini, B., De Iorio, M., Chong, M., Toh, J. Y., Michael, N., Gupta, V., Yap, F., Lee, Y. S., Eriksson, J., & Fogel, A. (2025). A Bayesian semi-parametric model for longitudinal growth and appetite phenotypes in children. arXiv preprint arXiv:2501.17040

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

Franzolini, B., De Iorio, M., Eriksson, J. (2023). Conditional partial exchangeability: a probabilistic framework for multi-view clustering. arXiv preprint doi:10.48550/arXiv.2307.01152

Marsh, W. A., Scarsbrook, L., Yüncü, E., Hodgson, L., Lin, A. T., De Iorio, M., Thalmann, O., Thomas, M. G., Bergström, A., Noseda, A., Amiri, S., Biglari, F., Borić, D., Bougiouri, K., Carmagnini, A., Giannì, M., Higham, T., Lebrasseur, O., Linderholm, A., Mannino, M. A., Middleton, C., Mustafaoğlu, G., Perri, A., Peters, J., Richards, M., Sarıtaş, Ö., Skoglund, P., Stevens, R. E., Stringer, C., Tabbada, K., Talbot, H. M., Van der Sluis, L. G., Bello, S. M., Dimitrijević, V., Martin, L., Mashkour, M., Parfitt, S. A., Vuković, S., Brace, S., Craig, O. E., Baird, D., Charlton, S., Larson, G., Barnes, I., & Frantz, L. A. F. (2025). Dogs were widely distributed across Western Eurasia during the Palaeolithic. Under revision for Nature

Mozdzen< A., Wertz, T., De Iorio, M., Cremaschi, A., Kastner, G., Eriksson, J. (2025). Repulsive mixtures via the sparsity-inducing partition prior. arXiv preprint arXiv:2509.25860

van den Boom, W., Cremaschi, A., and Thiery, A. H. (2024). Doubly adaptive importance sampling. doi:10.48550/arXiv.2404.18556

Forthcoming

Berquist, S., and Harris, A. (2026) Reframing Change: Agency and Metaphysics in the Societal Transformations of Theravadin Angkor, Cambodia and the Prehispanic Andes. Cambridge Archaeological Journal, accepted.

Beraha, M., Guglielmi, A., Quintana, F.A., De Iorio, M., Eriksson, J.G., and Yap, F. (2025). Childhood obesity in Singapore: a Bayesian nonparametric approach. Statistical Modelling, advance online publication. doi:10.1177/1471082X231185892

Cremaschi, A., Wertz, T.M., and De Iorio, M. (2025). Repulsion, chaos and equilibrium in mixture models. The Royal Statistical Society Series B, 87(2), 389-432.

Monteith, F., Harris, A., Chun, Y. (2026) Living Monumentality: The Socio-political landscapes of Big Buddha statues (dafo 大佛 in southern Sichuan, China (700-1200 CE). Cambridge Archaeological Journal, accepted.

Yan, Y., Jimené, B., Judge, M. T., Athersuch, T., De Iorio, M., & Ebbels, T. M. D. (2025). MetAssimulo 2.0: A web app for simulating realistic 1D & 2D metabolomic ¹H NMR spectra. Bioinformatics, 41(3), p.btaf045.

2026

Diekmann Y., Gillis, R.E., Lu, Z., Rudzinski, A., de Iorio, M. Thomas, M.G. (2026). Bayesian inference of sex-specific mortality profiles and product yields from unsexed cattle zooarchaeological remains. Journal of Archaeological Method and Theory, 33(1) doi:10.1007/s10816-025-09749-x

Lazarus, M.A., Franzolini, B., Eriksson, J.G., Chong, M.F.F., Ying, T.J., de Iorio, M., Meaney, M.J., Godfrey, K.M., Yap, F., Chen, H., Chong, Y.S., Kee, M.Z.L., Fogel, A, M. (2026). Feeding practices and concerns as mediators between maternal mental health and eating behaviours in early childhood Appetite, 217, 108340, ISSN 0195-6663, doi: 10.1016/j.appet.2025.108340

2025

Cabral, R., De Iorio, M., Harris, A. (2025). From Coin to Data: The Impact of Object Detection on Digital Numismatics. Digital Scholarship in the Humanities 40(3), 733–746, doi:10.1093/llc/fqaf046

Cabral, R., De Iorio, M., and Cremaschi, A. (2025). Where does the tail start? Inflection Points and Maximum Curvature as Boundaries. Stat, 14(3), doi:10.1002/sta4.70071

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

Harris, A., Cabral, R., Krajaejun, P., De Iorio, M., Kwa, C.G. (2025). Currents of Currency: Tracing Rising Sun/Srivatsa Coin Production and Distribution in First Millennium AD Southeast Asia through Die Studies. Antiquity, 99(406):1030-1048. doi:10.15184/aqy.2025.77

Mohapatra, L., Cabral, R. Bhatnagar, M., Chan, P.W. Ng, M., Chua, X.Y., Soon, C.S., Massar, S., de Iorio, M., Schmit, J.A.J (2025). Glucoregulatory status modulates acute cognitive effects of repeated low-glycaemic snack consumption in older adults: a decentralized randomized controlled trial. European Journal of Nutrition, 64(5), 189.

Tint, M.T., Cremaschi, A., Leow, M.K.S., Padmapriya, N., Ang, S.B., Lai, J.S., Chan, J.K.Y., Bernard, J.Y., Gluckman, P.D., Chong, Y.S. and Godfrey, K.M. (2025) Differential contributions of lean and fat mass on bone mineral density in Asian women of reproductive age: the Singapore Preconception Study of Long-Term Maternal and Child Outcomes study. JBMR plus, 9(6), doi: 10.1093/jbmrpl/ziaf054

2024

Cremaschi, A., De Iorio, M., Kothandaraman, N., Yap, F., Tint, M.T., and Eriksson, J. (2024). Joint modeling of association networks and longitudinal biomarkers: an application to childhood obesity. Statistics in Medicine, 43(6), 1135–1152. doi:10.1002/sim.9994

Cremaschi, A., Yang, W., De Iorio, M., Evans, W.E., Yang, J.J., and Rosner, G.L. (2024). Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia. Statistics in Biosciences, advance online publication. doi:10.1007/s12561-024-09437-6

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

Franzolini, B., Beskos, A., De Iorio, M., Poklewski Koziell, W., and Grzeszkiewicz, K. (2024). Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the U.S. stock market. The Annals of Applied Statistics, 18(1), 555–584. doi:10.1214/23-AOAS1801

Harris, A., Cremaschi, A., Lim, T.S., De Iorio, M., and Kwa, C.G. (2024). From past to future: digital methods towards artefact analysis. Digital Scholarship in the Humanities, 39(4), 1026-1042 doi:10.1093/llc/fqae057

Husain, S.F., Cremaschi, A., Suaini, N.H.A., De Iorio, M., Loo, E.X.L., Shek, L.P., Goh, A.E.N., Meaney, M.J., Tham, E.H., and Law, E.C. (2024). Maternal asthma symptoms during pregnancy on child behaviour and executive function: a Bayesian phenomics approach. Brain, Behavior, and Immunity, 118, 202–209. doi:10.1016/j.bbi.2024.02.028

Molinari, M., Cremaschi, A., De Iorio, M., Chaturvedi, N., Hughes, A.D., and Tillin, T. (2024). Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases. Journal of Applied Statistics, 51(1), 114–138. doi:10.1080/02664763.2022.2116746

Natarajan, A., De Iorio, M., Heinecke, A., Mayer, E., and Glenn, S. (2024). Cohesion and repulsion in Bayesian distance clustering. Journal of the American Statistical Association, 119(546), 1374–1384. doi:10.1080/01621459.2023.2191821

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

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), 508–509. doi:10.1007/s00134-024-07383-x

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

Salamanca-Sanabria, A., Liew, S.J., Mair, J., De Iorio, M., Young, D.Y.L., Tint, M.T., Yew, T.W., Lim, K., Ong, D., Chooi, Y.C., Tay, V., and Eriksson, J.G. A holistic lifestyle mobile health intervention for the prevention of type 2 diabetes and common mental disorders in Asian women with a history of gestational diabetes: a randomised control trial with 3-year follow-up protocol. Trials, 25, 443. doi:10.1186/s13063-024-08247-x

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

2023

Cremaschi, A., Argiento, R., De Iorio, M., Shirong, C., Chong, Y.S., Meaney, M.J., and Kee, M.Z. (2023) Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach. Bayesian Analysis, 18(3), 753–775. doi:10.1214/22-BA1326

De Iorio, M., Favaro, S., Guglielmi, A., and Ye, L. (2023) Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes. The Annals of Applied Statistics, 17(3), 2256–2278. doi:10.1214/22-AOAS1717

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

Franzolini, B., Lijoi, A., and Prunster, I. (2023) Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet processes. The Annals of Applied Statistics, 17(1), 313–332. doi:10.1214/22-AOAS1628

Harris, A., Tin, T., Chhay, R., and Vitou, P. (2023) Broken Buddhas, burials, and sanctuary-adjacent sanctuaries: ancestral animist archaeologies of Angkor’s ancient places and things. World Archaeology, 55(2), 167-188. doi:10.1080/00438243.2024.2354807

Harris, A. (2023) Sīmā boundary markers of Angkor: a critical reappraisal. Artibus Asiae, 82(2), 141-178.

Kee, M.Z.L., Cremaschi, A., De Iorio, M., Chen, H., Montreuil, T., Nguyen, T.V., Côté, S.M., O’Donnell, K.J., Giesbrecht, G.F., Letourneau, N., Chan, C.Y., and Meaney, M.J. (2023). Perinatal trajectories of maternal depressive symptoms in prospective, community-based cohorts across 3 continents. JAMA Network Open, 6(10), e2339942. doi:10.1001/jamanetworkopen.2023.39942

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

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

Yu, X., Nott, D.J., & Smith, M.S. (2023). Variational inference for cutting feedback in misspecified models. Statistical Science, 38(3), 490–509. doi:10.1214/23-STS886

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

2022

Argiento, R., and De Iorio, M. (2022). Is infinity that far? A Bayesian nonparametric perspective of finite mixture models. The Annals of Statistics 50(5), 2641–2663. doi:10.1214/22-AOS2201

Harris, A., Tina, T., Sreytouch, S., Horth, H., Vouchnea, C., and Somala, C. (2022). Towards a temporal understanding of Angkor Thom’s Theravada “Buddhist Terrace” archaeology. Asian Archaeology, 6, 167-183. doi:10.1007/s41826-022-00056-y

Molinari, M., Cremaschi, A., De Iorio, M., Chaturvedi, N., Hughes, A.D., and Tillin, T. (2022). Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5), 1181–1204. doi:10.1111/rssc.12570

Mozdzen, A., Cremaschi, A., Cadonna, A., Guglielmi, A., and Kastner, G. (2022). Bayesian modeling and clustering for spatio-temporal areal data: an application to Italian unemployment. Spatial Statistics, 52, 10715. doi:10.1016/j.spasta.2022.100715

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

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

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

2021

Cremaschi, A., De Iorio, M., Chong, Y.S., Meaney, M., and Kee, M. (2021). A Bayesian nonparametric approach to dynamic item-response modelling: an application to the GUSTO cohort study. Statistics in Medicine, 40(27), 6021–6037. doi:10.1002/sim.9167

Molinari, M., de Iorio, M., Chaturvedi, N., Hughes, A. and Tillin, T. (2021). Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study. The International Journal of Biostatistics, 17(1), 153–164. doi:10.1515/ijb-2019-0108