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I develop flexible machine-learning and AI methods for large scale data with the goal of uncovering hidden structures, with applications to cancer biology and ecology

Dissertation

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

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

Statistical methodology

  1. Zito, A., Rigon T., Roslin T., Niittynen, P., Hebert P. D. N., Zakharov, E. V., Ratnasingham, S., iBOL consortium, Ovaskainen O. and Dunson D. B. (2026+).
    Predicting global biodiversity via Hubbell regression
    Under review at Science bioRxiv Atlas

  2. Zito, A., Parmigiani, G. and Miller, J. W. (2026+).
    Poisson process factorization for modeling mutational processes along cancer genomes
    Journal of the American Statistical Association ACS, Major Revision arXiv GitHub Tutorial

  3. Zito, A. and Miller, J. W. (2026).
    Compressive Bayesian non-negative matrix factorization for mutational signature analysis
    Biometrics, in press arXiv GitHub

  4. Zito, A., Rigon, T., and Dunson, D. B. (2026).
    Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors
    Bayesian Analysis, 21(1), 139–166 Pub arXiv R package

  5. Lee, C. J., Zito, A., Sang, H., and Dunson, D. B. (2025).
    Logistic-Beta Processes for dependent random probabilities with beta marginals
    Bayesian Analysis, 20(4), 1345–1369 Pub arXiv GitHub
    Lindley Prize 2025 — honorable mention

  6. Xue, C., Zito, A. and Miller, J. W. (2024).
    Improved control of Dirichlet location and scale near the boundary
    Bayesian Analysis, R&R arXiv GitHub
    JSM 2025 SBSS student paper competition award — Cathy Xue

  7. 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 paper award, ISBA 2021 BEST award for Ph.D. student research 2022, Duke University

Agentic AI, decision theory and experimentation

  1. Parmigiani, G. and Zito, A. (2026).
    Multiverse analysis, Bayesian decision theory and agentic AI: drawing connections
    Invited submission to AStA Advances in Statistical Analysis Manuscript

  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

Applied work in ecology and cancer biology

  1. Modi, A., Zito, A. and Parmigiani, G. (2026).
    Pan-cancer genomic scars of alternative end joining and single-strand annealing
    BMC Cancer, in press

  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
    Methods in Ecology and Evolution, in press Pub bioRxiv

  3. 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, ISBA 2022

Refereed conference proceedings and discussions

  1. Lee, C. J., Zito, A. and Dunson, D. B. (2026).
    Discussion to “Dependent Dirichlet Processes” by MacEachern, S. N.
    Bayesian Analysis Pub

  2. 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

  3. 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

  4. 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 Part 1 Part 2

In preparation

  1. Zito, A., Parmigiani, G., Miller, J. W. and Samur, M. (2026+).
    Joint analysis of mutational process in DNA and RNA from multiple myeloma

  2. Qian, Z., Zito, A., Samur, M. and Parmigiani, G. (2026+).
    Temporal non-negative matrix factorization for diagnosis-relapse inference on mutational processes


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