Niccolo' Anceschi, Ph.D.
I am a Postdoctoral Associate in Statistics at Duke University in Durham, NC (USA).
My research focuses on the development of scalable Bayesian methods for interpretable latent structure discovery in high-dimensional, multimodal,
and heterogeneous data, with emphasis on uncertainty quantification and efficient inference — including MCMC, variational methods, and expectation propagation —
implemented in accessible open-source software.
Current and recent applied work includes collaborations with scientists at Merck Sharp & Dohme on multi-omics data integration for precision medicine,
and with academic investigators in environmental health, toxicology, and ecology.
This includes application-specific methodology development and simulation-based power analysis for studies of toxicant-induced neurodegeneration,
and statistical consulting for studies on air pollution-induced pulmonary inflammation and firefighters' exposure to harmful chemical mixtures.
Publications
Anceschi N., Rigon T., Zanella G. & Durante D. (2026)
Optimal and computationally tractable lower bounds for logistic log-likelihoods, Accepted for Publication in
Biometrika - preprint at
arXiv:2410.10309
Anceschi N., Ferrari F., Mallick H. and Dunson D. (2026)
Bayesian joint additive factor models for multiview learning, Accepted for Publication in
Biometrics - preprint at
arXiv:2406.00778
Poworoznek E.,
Anceschi N., Ferrari F. and Dunson D. (2025)
Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching, Bayesian Analysis, Advance Publication, 1-22, (2025)
Anceschi N., Fasano A., Durante D. and Zanella G. (2023)
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: a review and new results, Journal of the American Statistical Association, 118 (542), 1451-1469
Anceschi N., Hidalgo J., Plata. C., Bellini T., Maritan A., Suweis S. (2019)
Neutral and niche forces as drivers of species selection, Journal of Theoretical Biology 483, 109969
Preprints
Mauri L.,
Anceschi N., & Dunson D. (2026+)
Spectral decomposition-assisted multi-study factor analysis, arXiv:2502.14600
Anceschi N., Fasano A., Franzolini A. and Rebaudo G. (2026+)
Scalable expectation propagation for generalized linear models, arXiv:2407.02128