My research interests are statistics and machine learning, particularly Bayesian nonparametrics, regression, density estimation, clustering, feature allocation, dimension reduction, Gaussian process based models, MCMC, variational inference, and MAP inference. Applications of interest include the study of Alzheimer’s disease based on neuroimaging, biological, and clinical data, among others. See also my Google Scholar profile.
Submitted Papers:
- Wade, S.. (2022) "Bayesian Cluster Analysis".
- Harkonen, T., Wade, S., Law, K., and Roininen, L. (2022) "Mixtures of Gaussian process experts with SMC$^2$". arXiv
- Monterrubio-Gómez, K., and Wade, S. (2022) "On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach" arXiv
- Etienam, C., Law, K. and Wade, S., Zankin, V. (2022) "Ultra-fast Deep Mixtures of Gaussian Process Experts" arXiv
Publications:
- Leist, A.K., Klee, M., Kim, J.H., Rehkopf, D.H, Bordas, S.P.A, Muniz-Terrera, G., and Wade, S., (2022) "Machine Learning in Health and Social Sciences". Science Advances. arXiv
- Yu, W., Wade, S., Bondell, H.D., and Azizi, L. (2022) "Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data". Journal of Computational and Graphical Statistics link , arXiv, GitHub
- Teo, M. and Wade, S.. (2022) "Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models", BAYSM 2021 Selected Contributions, Springer.
- Gadd, C., Wade, S., and Shah, A. (2021) "Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models" Machine Learning. link, arXiv
- Wade, S., Piccarreta, R., Cremaschi, A. and Antoniano Villalobos, I. (2021) "Colombian Women's Life Patterns: A Multivariate Density Regression Approach". Bayesian Analysis. arXiv, github
- Gadd, C., Wade, S., Boukouvalas, A. (2020) "Enriched mixtures of Gaussian process experts." AISTATS, PMLR 108:3144-3154. link
- Monterrubio-Gómez, K., Roininen, L., Wade, S., Damoulas, T., and Girolami, M. (2020) "Posterior inference for sparse hierarchical non-stationary models." Computational Statistics & Data Analysis. link
- Wade, S. and Ghahramani, Z. (2017). "Bayesian cluster analysis: Point estimation and credible balls." Bayesian analysis, 13:559--626. link For code, see Software
- Prestia, A., Caroli, A., Wade, S., van der Flier, W.M., Ossenkoppele, R., Van Berckel, B., Barkhof, F., Teunissen C.E., Wall, A., Carter, S.F., Scholl, M., Choo, I.H., Nordberg, A., Scheltens, P., and Frisoni, G.B. (2015). “Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics”. Alzheimer’s & Dementia, 11:1191--1201. link
- Caroli, A., Prestia, A., Wade, S., Chen, K., Ayutyanont, N., Landau, S.M., Madison, C.M., Haense, C., Herholz, K., Reiman, E.M., Jagust, W.J., and Frisoni, G.B. (2015). “Alzheimer’s disease biomarkers as outcome measures for clinical trials in MCI”. Alzheimer’s Disease and Associated Disorders, 29:101--109. link
- Antoniano Villalobos I., Wade, S., and Walker S. G. (2014). “A Bayesian nonparametric regression model with normalized weights; A study of hippocampal atrophy in Alzheimer’s disease.” Journal of the American Statistical Association, 109:477--490. link
- Wade, S., Dunson D., Petrone S., Trippa, L. (2014). “Improving prediction from Dirichlet process mixtures via enrichment.” Journal of Machine Learning Research, 15:1041--1071. link
- Wade, S., Walker S. G., and Petrone S. (2014). “A predictive study of Dirichlet process mixture models for curve fitting.” Scandinavian Journal of Statistics, 41:580--605. link
- Wade, S., Mongelluzzo S., and Petrone S. (2011). “A enriched conjugate prior for Bayesian nonparametric inference.” Bayesian Analysis, 6:359--386. link
Books:
- Argiento, R., Durante, D., Wade, S. (Eds.) (2019) "Bayesian Statistics and New Generations: BAYSM 2018, Warwick, UK, July 2-3, Selected Contributions.” Springer Proceeings in Mathematics and Statistics. link
Other Contributions:
- Azizi, L., Wade, S., Yu, W. (2022) "Contributed Discussion: Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data by Kidd and Katzfuss” Bayesian Analysis, 17: 291–351. link
- Antoniano-Villalobos, I., Villa, C., Wade, S. (2021) "Contributed Discussion: Centered Partition Processes: Informative Priors for Clustering by Paganin et al.” Bayesian Analysis, 16: 346-347. link
- Avalos-Pacheco, A., De Vito, R., Wade, S. (2021) "Contributed Discussion: Centered Partition Processes: Informative Priors for Clustering by Paganin et al.” Bayesian Analysis, 16: 346-347. link
PhD Thesis
- Wade, S. (2013) "Bayesian nonparametric regression through mixture models”. pdf