• Title Assistant Professor of Computing and Data Sciences, Biomedical Engineering, Biology, and Bioinformatics
  • Education BS, Caltech
    PhD, MIT
  • Office MCS 138R
  • Web Address https://www.bu.edu/algo-bio-lab/
  • Area of Interest Tissue organization, cellular pathways, causal modeling, statistical learning
  • CV

Current Research

Our group works at the interface of the limits of algorithmic learning and the limits of biological experimentation in pursuit of the organizing principles of molecular, cellular and tissue processes.

A central vision of the lab is to study cellular pathways and tissue biology at scales that appear impossible to achieve, but which in fact are possible, by leveraging biological structure together with algorithmic and experimental designs that can exploit that structure. We apply this approach to discover systems and decipher their organization at three scales: regulatory networks acting intracellularly, networks acting between cells in a tissue microenvironment, and spatial organization across larger scales in a developmental process.

Selected Publications

  • Cleary, B., Simonton, B., Bezney, J., Murray, E., Alam, S., Sinha, A., Habibi, E., Marshall, J., Lander, E.S., Chen, F. and Regev, A., 2021. Compressed sensing for highly efficient imaging transcriptomics. Nature Biotechnology, 39(8), pp. 936-942.
  • Cleary, B., Cong, L., Cheung, A., Lander, E.S. and Regev, A., 2017. Efficient generation of transcriptomic profiles by random composite measurements. Cell, 171(6), pp. 1424-1436.
  • Cleary, B. and Regev, A., 2020. The necessity and power of random, under-sampled experiments in biology. arXiv preprint arXiv:2012.12961.
  • Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P. and Lee, L., 2019. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 176(4), pp. 928-943.
  • Einav, T. and Cleary, B., 2022. Extrapolating missing antibody-virus measurements across serological studies. Cell Systems, 13(7), pp. 561-573.
  • Hong, D., Dey, R., Lin, X., Cleary, B. and Dobriban, E., 2022. Group testing via hypergraph factorization applied to COVID-19. Nature Communications, 13(1), pp. 1-13.
  • Cleary, B., Hay, J.A., Blumenstiel, B., Harden, M., Cipicchio, M., Bezney, J., Simonton, B., Hong, D., Senghore, M., Sesay, A.K. and Gabriel, S., 2021. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings. Science translational medicine, 13(589), p. eabf1568.
  • Cleary, B., Brito, I.L., Huang, K., Gevers, D., Shea, T., Young, S. and Alm, E.J., 2015. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nature biotechnology, 33(10), pp. 1053-1060.

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