Publications

My publication list and selected preprints

Selected preprints/Accepted

[1] D. Xiang, M. Bogdan, J. Wallin, and D. Yekutieli. “Hierarchical Bayesian estimation of covariance matrices”. In: arXiv preprint arXiv:2606.24751 (2026).

[2] H. Wu, J. Wallin, and I. Ionita-Laza. “Scalable ultra-high-dimensional quantile regression with genomic applications”. In: arXiv preprint arXiv:2601.02826 (2026).

[3] A. B. Simas and J. Wallin. “Local asymptotic normality for spectral-tail likelihoods under fixed-domain sampling”. In: arXiv preprint arXiv:2610.09105 (2026).

[4] M. Kailas, M. Vihola, and J. Wallin. “Adaptive Riemannian manifold Hamiltonian Monte Carlo with hierarchical metric”. In: arXiv preprint arXiv:2604.09832 (2026).

[5] I. Hejný, G. Bonaccolto, P. Kremer, S. Paterlini, M. Bogdan, and J. Wallin. “Asymptotic theory for graphical SLOPE: Precision estimation and pattern convergence”. In: arXiv preprint arXiv:2604.12771 (2026).

[6] E. Giorgi and J. Wallin. “Bivariate geostatistical latent variable models for the analysis of antibody density data”. In: arXiv preprint arXiv:2608.27534 (2026).

[7] D. Bolin, A. B. Simas, and J. Wallin. “A bridge representation of Gaussian Whittle–Matérn fields on compact metric graphs”. In: arXiv preprint arXiv:2609.18375 (2026).

[8] D. Bolin, A. B. Simas, E. Karlsson Strandh, and J. Wallin. “Gaussian processes on directed metric graphs”. In: arXiv preprint arXiv:2609.01435 (2026).

[9] D. Bolin, X. Jin, A. B. Simas, and J. Wallin. “A unified and computationally efficient non-Gaussian statistical modeling framework”. In: arXiv preprint arXiv:2602.23987 (2026).

[10] E. Awadelkarim, D. Bolin, X. Jin, A. B. Simas, and J. Wallin. “Geometric ergodicity of Gibbs samplers for linear latent models with GIG variance mixtures”. In: arXiv preprint arXiv:2602.07944 (2026).

[11] A. Weinstein, J. Wallin, D. Yekutieli, and M. Bogdan. “Nonparametric shrinkage estimation in high dimensional GLMs via Polya trees”. In: Statistica Sinica (accepted) (2025). DOI: 10.5705/ss.202025.0221.

[12] C. Romano, J. Wallin, T. William, C. Drakeley, B. Francesco, M. Ciccozzi, and E. Giorgi. “Modelling serological multiplex bead assays responses: A case study from Malaysia”. In: medRxiv preprint (2025). DOI: 10.64898/2025.12.11.25342063.

[13] J. Larsson, M. Bogdan, K. Grzesiak, M. Massias, and J. Wallin. “Efficient solvers for SLOPE in R, Python, Julia, and C++”. In: arXiv preprint arXiv:2511.02430 (2025).

[14] I. Hejný, J. Wallin, and M. Bogdan. “Asymptotic distribution of low-dimensional patterns induced by non-differentiable regularizers under general loss functions”. In: arXiv preprint arXiv:2506.12621 (2025).

[15] E. Hartman, J. Wallin, J. Malmström, and J. Olsson. “Controllable protein design with particle-based Feynman–Kac steering”. In: arXiv preprint arXiv:2511.09216 (2025).

[16] M. Bogdan, A. Chojecki, I. Hejný, B. Kołodziejek, and J. Wallin. “Identifying network hubs with the partial correlation graphical LASSO”. In: arXiv preprint arXiv:2508.12258 (2025).

Articles

[1] M. O. Sewe, J. Wallin, J. Rocklöv, S. Wang, T. Koenigk, and J. C. Semenza. “Projecting tick-borne encephalitis risk in Sweden under climate change scenarios: a high-resolution spatio-temporal modeling approach”. In: Environmental Health (2026). DOI: 10.1186/s12940-026-01278-8.

[2] D. Saduakhas, D. Bolin, X. Jin, A. B. Simas, and J. Wallin. “Incorporating correlated nugget effects in multivariate spatial models: An application to Argo ocean data”. In: The Annals of Applied Statistics (2026). DOI: 10.1214/26-AOAS2194.

[3] I. Hejný, J. Wallin, M. Bogdan, and M. Kos. “Unveiling low-dimensional patterns induced by convex non-differentiable regularizers”. In: Annals of the Institute of Statistical Mathematics (2026). DOI: 10.1007/s10463-025-00957-6.

[4] J. Heidecke, H. K. Kriit, P. Fransson, J. Wallin, and J. Rocklöv. “Evaluating the ability of a thermal biology-informed reproduction number to explain patterns of West Nile incidence in Europe”. In: One Health (2026). DOI: 10.1016/j.onehlt.2026.101469.

[5] E. Hartman, J. Malmström, and J. Wallin. “Degradation graphs reveal hidden proteolytic activity in peptidomes”. In: PLOS Computational Biology (2026). DOI: 10.1371/journal.pcbi.1013972.

[6] E. Giorgi and J. Wallin. “A flexible class of latent variable models for the analysis of antibody response data”. In: Biostatistics (2026). DOI: 10.1093/biostatistics/kxag008.

[7] D. Bolin and J. Wallin. “Spatial self-confounding: smoothness-related estimation bias in spatial regression models”. In: Biometrika (2026). DOI: 10.1093/biomet/asaf076.

[8] D. Bolin, A. B. Simas, and J. Wallin. “Statistical inference for Gaussian Whittle–Matérn fields on metric graphs”. In: Journal of the Royal Statistical Society Series B: Statistical Methodology (2026). DOI: 10.1093/jrsssb/qkag074.

[9] D. Bolin, A. B. Simas, and J. Wallin. “Markov properties of Gaussian random fields on compact metric graphs”. In: Bernoulli (2026). DOI: 10.3150/25-BEJ1853.

[10] D. Bolin, A. B. Simas, and J. Wallin. “An explicit link between graphical models and Gaussian Markov random fields on metric graphs”. In: Stochastic Processes and their Applications (2026). DOI: 10.1016/j.spa.2026.104925.

[11] Y. Baurne, F. Delmar, and J. Wallin. “Heterogeneous variance models with Gaussian processes”. In: Psychological Methods (2026). DOI: 10.1037/met0000850.

[12] A. Omazic, G. Grandi, S. Widgren, J. Rocklöv, J. Wallin, J. C. Semenza, and N. Abiri. “Automated tick classification using deep learning and its associated challenges in citizen science”. In: Scientific Reports (2025). DOI: 10.1038/s41598-025-10265-x.

[13] Y. Liu, P. Fransson, J. Heidecke, P. Liyanage, J. Wallin, and J. Rocklöv. “An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka”. In: PLOS Computational Biology (2025). DOI: 10.1371/journal.pcbi.1013540.

[14] J. Larsson and J. Wallin. “The choice of normalization influences shrinkage in regularized regression”. In: Transactions on Machine Learning Research (2025).

[15] J. Heidecke, J. Wallin, P. Fransson, P. Singh, H. Sjödin, P. C. Stiles, M. Treskova, and J. Rocklöv. “Uncovering temperature sensitivity of West Nile virus transmission: Novel computational approaches to mosquito-pathogen trait responses”. In: PLOS Computational Biology (2025). DOI: 10.1371/journal.pcbi.1012866.

[16] S. Barman, J. C. Semenza, P. Singh, H. Sjödin, J. Rocklöv, and J. Wallin. “A climate and population dependent diffusion model forecasts the spread of Aedes albopictus mosquitoes in Europe”. In: Communications Earth & Environment (2025). DOI: 10.1038/s43247-025-02199-z.

[17] D. Bolin, A. B. Simas, and J. Wallin. “Gaussian Whittle–Matérn fields on metric graphs”. In: Bernoulli (2024).

[18] A. L. Ramadona, Y. Tozan, J. Wallin, L. Lazuardi, A. Utarini, and J. Rocklöv. “Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study”. In: The Lancet Regional Health-Southeast Asia (2023).

[19] H. Hedlund, H. Du Rietz, J. M. Johansson, H. C. Eriksson, W. Zedan, L. Huang, J. Wallin, and A. Wittrup. “Single-cell quantification and dose-response of cytosolic siRNA delivery”. In: Nature Communications (2023).

[20] Z. Farooq, H. Sjödin, J. C. Semenza, Y. Tozan, M. O. Sewe, J. Wallin, and J. Rocklöv. “European projections of West Nile virus transmission under climate change scenarios”. In: One Health (2023).

[21] A. Altmejd, J. Rocklöv, and J. Wallin. “Nowcasting COVID-19 Statistics Reported with Delay: A Case-Study of Sweden and the UK”. In: International Journal of Environmental Research and Public Health (2023).

[22] Z. Farooq, J. Rocklöv, J. Wallin, N. Abiri, M. O. Sewe, H. Sjödin, and J. C. Semenza. “Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers”. In: The Lancet Regional Health-Europe (2022).

[23] F. Delmar, J. Wallin, and A. M. Nofal. “Modeling new-firm growth and survival with panel data using event magnitude regression”. In: Journal of Business Venturing (2022).

[24] A. Bukartas, J. Wallin, R. Finck, and C. Rääf. “Accuracy of a Bayesian technique to estimate position and activity of orphan gamma-ray sources by mobile gamma spectrometry: Influence of imprecisions in positioning systems and computational approximations”. In: Plos one (2022).

[25] D. Bolin and J. Wallin. “Local scale invariance and robustness of proper scoring rules”. In: Statistical Science (2022).

[26] J. Wallin, M. Bogdan, P. A. Szulc, R. Doerge, and D. O. Siegmund. “Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects”. In: Genetics (2021).

[27] L. M. McFetridge, Ö. Asar, and J. Wallin. “Robust joint modelling of longitudinal and survival data: Incorporating a time-varying degrees-of-freedom parameter”. In: Biometrical Journal (2021).

[28] A. Bukartas, J. Wallin, R. Finck, and C. Rääf. “Bayesian algorithm to estimate position and activity of an orphan gamma source utilizing multiple detectors in a mobile gamma spectrometry system”. In: PloS one (2021).

[29] M. T. Parente, J. Wallin, and B. Wohlmuth. “Generalized bounds for active subspaces”. In: Electronic Journal of Statistics (2020).

[30] D. Bolin and J. Wallin. “Multivariate type G Matérn stochastic partial differential equation random fields”. In: Journal of the Royal Statistical Society. Series B: Statistical Methodology (2020).

[31] Ö. Asar, D. Bolin, P. J. Diggle, and J. Wallin. “Linear mixed effects models for non-Gaussian continuous repeated measurement data”. In: Journal of the Royal Statistical Society: Series C (Applied Statistics) (2020).

[32] A. Bukartas, R. Finck, J. Wallin, and C. Rääf. “A Bayesian method to localize lost gamma sources”. In: Applied Radiation and Isotopes (2019).

[33] D. Bolin, J. Wallin, and F. Lindgren. “Latent Gaussian random field mixture models”. In: Computational Statistics & Data Analysis (2019).

[34] J. Wallin and D. Bolin. “Efficient adaptive MCMC through precision estimation”. In: Journal of Computational and Graphical Statistics (2018).

[35] A. Hildeman, D. Bolin, J. Wallin, and J. B. Illian. “Level set Cox processes”. In: Spatial statistics (2018).

[36] B. Gunnarsson, J. Wallin, and J. Klingberg. “Predation by avian insectivores on caterpillars is linked to leaf damage on oak (Quercus robur)”. In: Oecologia (2018).

[37] K. Podgórski and J. Wallin. “Convolution-invariant subclasses of generalized hyperbolic distributions”. In: Communications in Statistics-Theory and Methods (2016).

[38] W. Mao, I. Rychlik, J. Wallin, and G. Storhaug. “Statistical models for the speed prediction of a container ship”. In: Ocean engineering (2016).

[39] R. Maghsood, P. Johannesson, and J. Wallin. “Detection of steering events using hidden Markov models with multivariate observations”. In: International Journal of Vehicle Systems Modelling and Testing (2016).

[40] K. Johnsson, J. Wallin, and M. Fontes. “BayesFlow: latent modeling of flow cytometry cell populations”. In: BMC bioinformatics (2016).

[41] D. Bolin and J. Wallin. “Spatially adaptive covariance tapering”. In: Spatial Statistics (2016).

[42] D. Bolin, A. Frigessi, P. Guttorp, O. Haug, E. Orskaug, I. Scheel, and J. Wallin. “Calibrating regionally downscaled precipitation over Norway through quantile-based approaches”. In: Advances in Statistical Climatology, Meteorology and Oceanography (2016).

[43] J. Wallin and D. Bolin. “Geostatistical modelling using non-Gaussian Matérn fields”. In: Scandinavian Journal of Statistics (2015).

[44] K. Podgórski and J. Wallin. “Maximizing leave-one-out likelihood for the location parameter of unbounded densities”. In: Annals of the Institute of Statistical Mathematics (2015).

[45] K. Podgórski, I. Rychlik, and J. Wallin. “Slepian models for moving averages driven by a non-Gaussian noise”. In: Extremes (2015).

[46] R. Maghsood, I. Rychlik, and J. Wallin. “Modeling extreme loads acting on steering components using driving events”. In: Probabilistic Engineering Mechanics (2015).

[47] S. I. Adalbjörnsson, J. Swärd, J. Wallin, and A. Jakobsson. “Estimating periodicities in symbolic sequences using sparse modeling”. In: IEEE Transactions on Signal Processing (2015).

Proceedings

[1] J. Larsson, Q. Klopfenstein, M. Massias, and J. Wallin. “Coordinate descent for slope”. In: International Conference on Artificial Intelligence and Statistics. PMLR. 2023.

[2] Y. Baurne, F. Delmar, J. Wallin, and A. Brattstrom. “How Significant Events and Team Trust Predict Member’s Exit in New Venture Teams”. In: Academy of Management Proceedings. Academy of Management Briarcliff Manor, NY 10510. 2023.

[3] J. Larsson and J. Wallin. “The Hessian Screening Rule”. In: Advances in Neural Information Processing Systems. 2022.

[4] D. Bolin and J. Wallin. “Efficient methods for Gaussian Markov random fields under sparse linear constraints”. In: Advances in Neural Information Processing Systems. 2021.

[5] J. Larsson, M. Bogdan, and J. Wallin. “The Strong Screening Rule for SLOPE”. In: Advances in Neural Information Processing Systems. Ed. by H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan and H. Lin. Curran Associates, Inc., 2020.

[6] F. Delmar and J. Wallin. “Modelling new firm growth and survival: Some practical solutions”. In: Academy of Management Proceedings. Academy of Management Briarcliff Manor, NY 10510. 2018.