Independently developed an original LSTM-based recurrent neural network for forecasting time-series ionospheric scintillation events to a RMSE of 0.031, displaying the ability of neural networks to predict stochastic processes that are otherwise difficult to model.

A paper showcasing these original findings has been accepted for a presentation at the URSI Boulder Conference for Radio Science in 2023

Analyzed digital signal processing code runtimes, implemented small digital beamforming tasks, assisted with data collections and created a GNURadio flowgraph to simulate quadrature phase shift keying in a digital communications link. Applied to two small projects for an array sensor testbed and a communications simulation.

Selected as one of two composers for the apprentice composer position, wrote an orchestra piece to be performed by NYO-USA. Worked with composer Sean Shepherd during the writing process.

Also will attend a 3-week residency at Purchase college to refine musical skills, rehearse pieces, and attend workshops.

The residency and the performance have been postponed until 2021.

Worked as an intern in the ASPIRE program, creating a research project based around software-defined radio and its applicability in UAV detection. Studied principles of modern radars and configured a software radar in GNURadio. Completed live testing and data collection using the software radar on a quadcopter drone. Wrote a paper and presented about the advantages of software radars.