A personal webpage used to host my academic work (last updated March 2018).
My professional interests are in machine learning and mathematical physics. The primary focus of my PhD is in axiomatically formulating neural network operations on data manifolds using techniques from modern geometry. Less theoretical work in my PhD involves probabilistically forecasting time series using tools from neural networks, digital signal processing and manifold learning. This webpage is currently under construction.
I studied honours physics and mathematics at the University of Toronto (BSc honours), and am currently a PhD student at Penn State in Mechanical and Nuclear engineering. The title of my thesis is "Principles of Riemannian Geometry in Neural Networks" and I defended in March, 2018.