Graduate Research Assistant

• Aug 2021 - Present

Member of the Systems Neural Engineering Lab (PI: Chethan Pandarinath, PhD) Member of the Systems Neural Engineering Lab (PI: Chethan Pandarinath, PhD)

Undergraduate Researcher, Physics

• Jun 2020 - May 2021

This is a collaborative initiative to improve the accuracy of gravitational waveforms produced from a computationally efficient approximated model of an extreme-mass-ratio inspiral system which uses a method of smoothing oscillatory solutions to the original system of differential equations known as a near-identity transformation. Our main investigation is focused on improving the accuracy of the approximated model in the case of highly eccentric orbits. This role entails working with models of dynamical systems of nonlinear differential equations as well as conducting Fourier analyses of the resulting waveforms of the simulated inspiral system. This project received funding from the NASA New York Space Grant Consortium and the Research Foundation for SUNY.

Undergraduate Researcher, Neuroscience

• Apr 2019 - May 2021

This research is an investigation into the cognitive and neural basis of spatial memory development in humans. This work seeks to establish evidence for how spatial memories are recalled cognitively and to better understand both how these environments are represented in the brain and how the representations of these environments evolve after learning. My role entails developing statistical and multi-voxel pattern analysis methods for fMRI data analysis for use in analyzing activation patterns in brain areas such as the hippocampus in order to determine the extent and manner in which different brain structures are utilized while learning and navigating new environments.

Laboratory Instructor, Physics

• Jun 2018 - May 2021

Undergraduate Researcher, Applied Mathematics

• May 2018 - Jul 2019

This research was in the field of derivative-free optimization and sought to quantify algorithm performance in cases which required a large computational budget, such as functions with multimodal, non-convex and/or non-differentiable parameter spaces as well as high dimensional parameter spaces. These metrics can be used to effectively determine specific preferences and weaknesses of different approaches for a wide variety of cases, allowing for the identification of the contextual strengths of different methods.

Student Presenter at the MAA Seaway Section Meeting

• Oct 2018 - Oct 2018

Title: "Comparison of derivative-free optimization algorithms using a generalized characteristic metric" Title: "Comparison of derivative-free optimization algorithms using a generalized characteristic metric"

Cashier

• Jan 2016 - May 2017