One of the most common tools to recover the phase information of unstained microscopic translucent samples is Digital Holographic Microscopy (DHM). This imaging technique has a broad number of applications in biology and biomedicine. Nonetheless, to reconstruct an aberration-free phase image using DHM, a computationally demanding numerical process must be precisely executed. In this contribution, we present a generative adversarial network to fully compensate and reconstruct DHM holograms without the need for any computational process directly from the recorded hologram.

Optical imaging can utilize various optical phenomena to holistically probe an object’s properties with multiple dimensions of measurement. Unfortunately, optical imaging is limited by tissue scattering to imaging only outer superficial layers of an organism, or specific components isolated from within the organism and prepared in-vitro. I present recent developments in computational microscopy that enable 1) multi-dimensional 3D optical imaging with phase and fluorescence contrast; and 2) 3D refractive-index imaging of multiple-scattering samples. I will discuss the computational frameworks that underpin these applications, which are based on large-scale nonlinear and nonconvex optimization. Lastly, I will discuss extensions of this research for non-biological applications.

This proposed research aims to study the preventative measures of sunscreen on skin diseases. Our proposed procedure for monitoring sunshine protection is through mathematical modeling in which the Lotka-Volterra predator and prey interaction model is applied to analyze the system. The results intend to show the future trends of sunscreen vs. skin disease(sunburn, skin cancer, etc.) and to reduce the risk of skin cancer in specific geographic areas depending on some risk factors (genetics, different races, UV exposure, etc). Possible progress in future skin protection efforts will be depicted through solving the model with exact or simulated solutions.