I am originally from south Jersey. For my undergraduate, I attended Rensselaer Polytechnic Institute in Troy, NY for Physics and Applied Mathematics. Upon graduating, I moved to Los Angeles in the summer of 2016 to pursue a PhD in applied mathematics at UCLA. Currently, I am a second year graduate student in the program, and I have passed all of my qualifying exams.
In addition to being a graduate student, I also do private tutoring for high school level to entry graduate level mathematics. For more information, contact me!
Some recent interests of mine have been optimization problems in machine learning, model-order reduction methods, and scientific computing. Many of these interests are new to me, so I am looking forward to what these fields may hold. One things I hope to learn this year in addition to these topics is stochastic processes and their applications in financial modeling.
At Rensselaer, I primarily did research on the evolution of thin film morphology. Here we were interested in developing a model of nanosurface growth under high ambient pressure, which has previously been found to result in time invariant surface roughness, a novel property for thin films to display. This model was then tested and verified by Monte Carlo simulations. Publications and results have been listed in my curriculum vitae found below. Other projects I worked on were computationally determining the universality class scaling coefficients for the stochastic KPZ growth equation in (2+1) dimensions, and studying scaling coefficients for diffusion limited aggregation.
Other projects I have worked on briefly include mathematically modeling neuron networks, and applying empirical dynamical modeling techniques to plasma thruster simulations. The former was a project I continued in the RPI mathematics department, where I implemented the leaky integrate-and-fire model on complex networks with intention of applying it to modeling spike time dependent plasticity. The latter was my research during the summer of 2017 at Edwards Air Force Research Lab. This research was a proof of concept study to show that methods in compressed sensing and optimization can be used to determine low dimensional models of dynamical systems directly from real, noisy time series measurements.