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Preprint. Large-scale benchmarks to assess models for RNA fitness & structure prediction.

Preprint. A multiplexed experimental approach to analyze conformational fluctuations across thousands of protein domains, revealing hidden variations that affect protein cooperativity and function.

Preprint. A conditional autoregressive transformer model trained on 14.6 million mammalian promoter sequences that achieves state-of-the-art performance in predicting the effects of indels in human promoter regions.

NeurIPS, LMRL, 2022. A hybrid family-specific and family-agnostic model to achieve SOTA performance on protein fitness prediction and human variant annotation.

ICML, WCB, 2022. The first paper investigating scaling laws in protein language modeling.

We leverage deep generative models of evolutionary sequences to predict viral escape mutations.

Preprint. A memory-efficient dropout-inspired scheme to train large neural networks faster with no loss in accuracy.

We introduce an importance sampling-based estimator to estimate the epistemic uncertainty of deep learning models for high-dimensional discrete datasets.