A personal webpage used to host my academic work (last updated August 2019).
My professional interests are in machine learning and engineering physics. My PhD involved formulating neural network operations on data manifolds using techniques from modern geometry, as well as probabilistically forecasting time series using tools from neural networks, digital signal processing and manifold learning. The title of my thesis is "Principles of Riemannian Geometry in Neural Networks" and I defended in March, 2018. I am currently a postdoctoral fellow at the University of Pennsylvania's Children Hospital of Philadelphia studying machine learning.
Traditional academic papers I've written, mostly in machine learning and geometry.
Read MoreInformal notes on machine learning and mathematics, as well as some very helpful external resources.
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