University of Cambridge > > Machine Learning Reading Group @ CUED > Deciphering Batch Effects in Single-cell Transcriptomics with Concept Bottlenecks

Deciphering Batch Effects in Single-cell Transcriptomics with Concept Bottlenecks

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If you have a question about this talk, please contact Isaac Reid.

Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] Sign up to our mailing list for easier reminders via

In this presentation, I’ll demystify the intersection of AI/ML + Genomics, examining data types, problems, and applications. I’ll share my views on potential future directions and discuss my recent PhD work on building foundation models for science.

The highlight is our proposed model, GeneCBM, designed to account for the origins and outcomes of unwanted variance in genomics data. This model incorporates three key features: metadata prediction, data generation, and atlas integration.

This talk is part of the Machine Learning Reading Group @ CUED series.

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