אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In biological imaging, fast acquisition of depth information is crucial e.g. for accurate 3D tracking of sub-cellular elements and for 3D super-resolution. In the first part of this talk, we present a series of works enhancing the success of snapshot depth sensing in the revolutionary field of single-molecule localization microscopy (Nobel Prize in Chemistry 2014). Specifically, we present an approach for jointly designing the “optics” of the microscope and the 3D reconstruction algorithm, by using deep learning. Our approach is demonstrated experimentally with super-resolution reconstructions of mitochondria and volumetric imaging and tracking of fluorescently labelled telomeres in live cancer cells. In the second part of this talk, we focus on the challenging task of visualizing and quantifying reconstruction uncertainty in computational imaging systems. Specifically, we present two techniques for visualizing prediction uncertainty in imaging inverse problems. Our methods are at least as accurate, and orders of magnitude faster than baselines that rely on state-of-the-art posterior samplers. We demonstrate the practical benefit of our methods for severely ill-posed inverse problems, including transferring the images of a biological sample imaged with one fluorescent dye to appear as if they were imaged with another.

Elias Nehme is a PhD candidate at the ECE department, jointly supervised by prof. Tomer Michaeli and prof. Yoav Shechtman. Prior to that he received his bachelor’s in biomedical engineering also from the Technion. His awards and honors include the Lev-Margulis memorial prize in microscopy, the best poster award in quantitative bioimaging, the Jacobs-Qualcomm fellowship in 3D imaging and reconstruction, and several VATAT prizes in data science.

Ph.D. Under the supervision of Prof. Tomer Michaeli and Prof. Yoav Shechtman.