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My research interests are primarily in machine learning using statistical methods. I am interested in nonlinear dimension reduction and manifold learning. Additionally, I am interested in time series analysis methods including spectral analysis of time series as well as wavelet analysis of times seires. I am also interested in online recommendation algorithms having designed and implemented the recommendation algorithm for ModeSens, an online fashion website currently in closed beta. Currently I'm working in the area of Manifold Learning. Part of my research involved building the (open source) scalable manifold learning package Megaman in python. The pre-print can be found on arXiv: megaman: Manifold Learning with Millions of points, it will be featured in JMLR 2016. Finally, my thesis research also invovlves developing a mathematically principled manifold learning algorithm that approaches isometry by directly optimizing the push-forward Reimannian Metric which was published as "Nearly Isometric Embedding by Relaxation" in NIPS 2016.


An Analysis of "Locally Defined Principal Curves and Surfaces"

megaman: Manifold Learning with Millions of points -- JMLR 2016

Nearly Isometric Embedding by Relaxation - NIPS 2016