8 citations · 17 across the 4 of their papers we have counts for
4 papers
Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
Samuel J. Yang, Shutong Li, Subhashini Venugopalan +5
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such…
Physics-enhanced machine learning for virtual fluorescence microscopy
Colin L. Cooke, Fanjie Kong, Amey Chaware +7
This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy. Our results show that by including a model of illumination within the first…
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Subhashini Venugopalan, Arunachalam Narayanaswamy, Samuel Yang +10
Confounding variables are a well known source of nuisance in biomedical studies. They present an even greater challenge when we combine them with black-box machine learning techniq…
Correcting Nuisance Variation using Wasserstein Distance
Gil Tabak, Minjie Fan, Samuel J. Yang +2
Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells. One motivating application…