Research highlights

I am strongly motivated by application, which serves as a base for developing new statistical methodologies. My goal is to create simple yet effective methods that are theoretically justified and useful to practitioners who need to draw scientific conclusions.

My current line of work involves improving on Bayesian non-negative matrix factorization algorithms, with a strong emphasis on cancer genomics and mutational signatures analysis. I am also developing regression models for biodiversity estimation.

During my Ph.D. at Duke, I developed Bayesian nonparametric methodologies under the supervision of Professor David Dunson in Project LIFEPLAN, an international research group whose aim is to quantify biodiversity across the globe.

In recognition of my doctoral dissertation, I received the Savage Award in Applied Methodology at the 2025 Joint Statistical Meeting. Read it below!

Ph.D. Dissertation Ph.D. defense slides Google Scholar

Publications and manuscripts

Preprints

  1. Zito, A., Parmigiani, G. and Miller, J. W. (2025). Poisson process factorization for mutational signature analysis with genomic covariates. Submitted. [arXiv]

  2. Orsholm, J.*, Zito, A.*, Somervuo, P., Braga M, Chazlot, N, Roslin, T. and Furneaux, B. (2025). Discovering the unseen: a performance comparison of taxonomic classification methods under unknown DNA barcodes. Submitted. [bioaRxiv]

  3. Xue, C., Zito, A. and Miller, J. W. (2024). Improved control of Dirichlet location and scale near the boundary. Invited Reject and Resubmit from Bayesian Analysis [ArXiv] [GitHub] - JSM 2025 SBSS student paper competition award received by Cathy Xue

  4. Zito, A. and Miller, J. W. (2024). Compressive Bayesian non-negative matrix factorization for mutational signature analysis. Under review at Biometrics Methodology. [ArXiv] [GitHub]

Articles in refereed journals

  1. Lee, C. J., Zito, A., Sang, H., and Dunson, D. B. (2025). Logistic-Beta Processes for dependent random probabilities with beta marginals. Bayesian Analysis, in press [ArXiv] [GitHub]

  2. Zito, A., Greaves, D., Soriano, J. and Richardson, L. (2025). Pareto optimal proxy metrics. Applied Stochastic Models in Business and Industry, 41: e70003 [Pub][ArXiv]

  3. Zito, A., Rigon, T., and Dunson, D. B. (2024). Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors. Bayesian Analysis, [Pub][ArXiv] [R Package]

  4. Zito, A., Rigon, T., and Dunson, D. B. (2023). Inferring taxonomic placement from DNA barcoding aiding in discovery of new taxa. Methods in Ecology and Evolution, 14, 529–542. [Pub] [ArXiv] [Code] [R package]. - Best poster award at IBSA2022 world meeting.

  5. Zito, A., Rigon, T., Ovaskainen, O., and Dunson, D. B. (2023). Bayesian modeling of sequential discoveries. Journal of the American Statistical Association (Theory & Methods), 118(544), 2521–2532. [Pub] [Manuscript] [R package] - Best Student/Postdoc Contributed paper award at IBSA2021 world meeting. - BEST award for Ph.D. student research 2022, Dept. of Statistical Science, Duke University

Refereed conference proceedings and discussions

  1. Zito, A., Xue, C., and Miller, J. W. (2025). Accounting for Transcriptional Asymmetries in Mutational Signature Analysis Using Compressive Bayesian Non-negative Matrix Factorization In: di Bella, E., Gioia, V., Lagazio, C., Zaccarin, S. (eds) *Statistics for Innovation IV. SIS 2025. Italian Statistical Society Series on Advances in Statistics. Springer, Cham.

  2. Zito, A. and Miller, J. W. (2024). Discussion to “Sparse Bayesian factor analysis when the number of factors is unknown” by Fruhwirth–Schnatter, S., Hosszejini, D., and Lopes, F. L., Bayesian Analysis [Pub]

  3. Zito, A., Rigon, T. and Dunson, D. B. (2021). Modelling of accumulation curves through Weibull survival functions. In Book of Short Papers of the Italian Statistical Society 2021 (Perna, C., Salvati, N. and Schirripa Spagnolo, F., editors). ISBN: 9788891927361. [Link part 1] [Link part 2]

Manuscripts in preparation

  1. Zito, A., Rigon T., Niittynen and Roslin T., Niittynen, P., Hebert P. D. N., the iBOL consortium, Ovaskainen O. and Dunson D. B. (2025). Coviariate-dependent biodiversity measures via the Hubbell regression. expected submission: December 2025

  2. Zito, A., Parmigiani, G., Miller, J. W. and Samur, M. (2025+). Joint analysis of mutational process in DNA and RNA from multiple myeloma.


Address:
655 Huntington Ave, Boston, MA 02115, United States
Email:
azito@hsph.harvard.edu
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