CellDISECT

Jun 3, 2025
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Deep Learning Single-Cell Genomics Causal Inference
Arian Amani
Arian Amani
Machine Learning Scientist

I am a Machine Learning Scientist at AI VIVO and a Data Scientist at the Wellcome Sanger Institute. My work is at the intersection of computational biology and drug discovery, where I develop deep generative and foundation models for molecules and cells. I specialize in molecule generation and single-cell perturbation modeling using advanced techniques like VAEs, Diffusions, Transformers, and Flow Matchings. I’m passionate about building AI methods that accelerate target discovery and therapeutic design.

Publications

Integrating multi-covariate disentanglement with counterfactual analysis on synthetic data enables cell type discovery and counterfactual predictions
CellDISECT is a powerful causal generative model that enhances single-cell analysis by disentangling variations, making counterfactual predictions, and achieving flexible fairness.
Stathis Megas, Arian Amani, Antony Rose, Olli Dufva, Kian Shamsaie, Hesam Asadollahzadeh, Krzysztof Polanski, Muzlifah Haniffa, Sarah Amalia Teichmann, Mohammad Lotfollahi
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Integrating multi-covariate disentanglement with counterfactual analysis on synthetic data enables cell type discovery and counterfactual predictions