Yoav Shechtman

This paper demonstrates a simple 3D single-molecule localization microscopy technique using 3D-printed phase masks and computational compensation.

This paper introduces SoLiD-3D, a high-speed volumetric SMLM platform that improves localization density and axial resolution using dual-wavelength light sheets and engineered PSFs.

The paper tracks DNA repair dynamics in yeast using high-throughput methods, revealing chromatin reconfiguration linked to single- and double-strand breaks.

The paper introduces a physics-informed generative model to create realistic training data for localization microscopy, improving performance in challenging conditions.

The paper introduces AutoDS and AutoDS3D, deep learning tools for single-molecule super-resolution microscopy that reduce manual intervention and computation time.

This paper introduces long axial-range double-helix PSFs for 3D super-resolution imaging, enabling stitching-free imaging of whole mammalian cells with improved speed and resolution.

OM2Seq improves OGM data analysis by using a Transformer-encoder to rapidly and accurately map DNA fragments to a reference genome, outperforming existing methods in speed and accuracy.

This paper develops an information theory-based model to design optimal labeling patterns for optical genome mapping, improving accuracy by up to 10-fold in human and bacterial DNA.

DeepOM uses deep learning to improve alignment of single-molecule optical genome mapping images to genomic references, showing better performance for shorter DNA molecules.

ZeroCostDL4Mic provides a platform to use deep learning in microscopy, enabling tasks like segmentation and denoising through Google Colab.