Shahriar Esmaeili is a graduate student in the Institute for Quantum Science and Engineering (IQSE) at the Department of Physics and Astronomy under the supervision of Profs. Marlan Scully and Philip Hemmer, and a Hagler Institute for Advanced Study HEEP fellowship program (HIAS HEEP) fellow at Texas A&M University.
Shahriar's research includes paradigms from biophotonics, quantum optics, and material science alongside innovative imaging techniques for non-invasive live imaging with fluorescent tags and activation of light-sensing proteins, and developing DNA detection methods using FRET coupling between nanoparticles like Gold nanoparticles (AuNPs), Upconversion nanoparticles (UCNPs), and Nanodiamonds.
Combining knowledge from disparate disciplines can be uniquely effective in building new models to study the role of specific neurons optogenetically using the applications of UCNPs, and the other discipline Shahriar has focused on is studying the application of nanotechnology in neuroscience in order to answer questions ranging from broad questions regarding neurological disorders to very precise questions about brain activity mapping.
Shahriar has also done his master's degree in computational plasma astrophysics. Shahriar developed a numerical solution method to solve Magnetohydrodynamics (MHD) equations in solar coronal loops. One of the main challenges in data classification is dealing with noisy data, specifically in solar dataset. As an interdisciplinary researcher, Shahriar has also proposed a method based on the maximum margin between two classes. Their proposed method ignores the effect of outliers and noises, so their method has the widest margin compared with other large-margin classifiers; also, their convex class model is more robust compared with the support vector machine (SVM).