Computational biology · machine learning
Tanmay Tanna
I build computational methods for measuring and modelling cellular responses.
I develop statistical and machine-learning approaches for emerging biological measurement technologies, with current work spanning in vivo perturbation screens, CRISPR-based transcriptional recording and cancer multi-omics.
Research focus
Methods grounded in biological questions
I apply statistical methodology and machine learning across experimental domains: cancer biology, genetic perturbations and transcriptional recording, with an emphasis on approaches that remain useful beyond a single dataset.
Perturbation modelling and in vivo screens
Designing genome-scale perturbation experiments and developing statistical and machine-learning methods to interpret their effects in living tissue.
Read moreTranscriptional recording
Computational methods for CRISPR-based systems that record cellular transcriptional histories as molecular data.
Read moreMulti-omics method development
Statistical and algorithmic methods for metabolomics, metagenomics and multimodal data.
Read moreComputational cancer research
Multimodal analysis of clinically annotated cancer cohorts, with work in melanoma and ovarian cancer.
Read moreSelected outputs
Recent and foundational work
Principal contributions to research spanning transcriptional recording, single-cell perturbation screens, cancer multi-omics and computational method development.
Recording transcriptional histories using Record-seq
Nature Protocols
Perturbation-aware representation learning for in vivo genetic screens
NeurIPS 2025, AI4D3 workshop
An end-to-end computational framework for ‘Record-seq’ transcriptional recording data
Bioinformatics · Accepted
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