Eli N. Weinstein

The paper introduces manufacturing-aware generative models to enable efficient biological sequence design and synthesis at petascale, reducing costs by a trillion-fold.

The paper introduces a black-box algorithm to optimize stochastic DNA synthesis protocols for generating samples from generative sequence models, showing significant improvements in hit rates for protein engineering.

The paper introduces a Bayesian generative model for whole genomes, showing improved predictive performance over parametric models across genomic, transcriptomic, and metagenomic data.

This paper introduces a structured observation distribution (MuE) to improve generative probabilistic modeling of biological sequences, showing it outperforms methods using multiple sequence alignment preprocessing in sequence prediction and forecasting.

The paper introduces an all-optical electrophysiology method called Optopatch to functionally characterize human iPSC-derived motor neurons in ALS models, finding altered electrophysiological properties in SOD1 A4V mutants.