West Greenwich, Rhode Island, United States
I am a masters student at Columbia University Financial Engineering. I have formal experience in AI, Physics, Philosophy, Coding, and Painting. I have informal experience in casual conversation and being tall. Having studied Physics for 3.5 years, I have experience processing complex data, thinking creatively and analytically, and dealing with intricate concepts. These skills can be exemplified in two of my research projects - one studying black hole thermodynamics which dealt with challenging applications of differential geometry, and another applying the physics-based Fourier transform to financial time series GANs. I also have a year of experience as a Physics TA, preparing me to communicate difficult concepts effectively from every angle.
I am leading several projects researching AI applications in finance, including building a GPT for financial time series forecasting and a frequency-space GAN architecture for synthetic financial time series replication. Results for both projects are promising.
I worked in a small group of two researching an AI interpretability problem: using the LIME interpretability framework to classify whether data is "pristine" vs. whether it has been adversarially attacked (either with statistical noise or with model-conscious methods such as FGSM noise). We achieved impressive results, including over 99% accuracy classifying Gaussian noise on the ImageNet v2 dataset and accuracies well over 80% for several other types of noise. A paper on this work is in the final editing stages and will be published shortly.
Led several self-driven research projects on AI applications in finance, including order slicing and financial time series GANs. Presented research findings/updates in weekly meetings to the project manager.
I was a TA for two introductory Physics courses during my time at UMass. One of the courses was a Matlab based computational physics course, focused on data analytics and simulations/modeling. The courses had 80 and 25 students, respectively. I was responsible for both grading weekly assignments and helping students grasp difficult concepts. This was a position that I enjoyed immensely and learned a lot from, both from the professors teaching the courses and from the act of teaching myself. This prepared me to effectively communicate difficult concepts to novices.