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Research Statement
In the future, I want to further investigate deep learning from a statistical machine learning perspective. I want to better understand the dynamics of training with gradient descent on deep networks as well as further investigate their representative power with particular architectures and specific datasets. In particular, I want to assess the interplay between training and validating and how the assumptions we originally make align with the studied regimes of neural networks. I believe these questions are fundamental to every model we use, however, neural network's incredible functionality have distracted us from answering these fundamental questions.

Hierarchical Semantic Labeling with Adaptive Confidence (Computer Vision) (2019)
Figure of Main Idea
The Perfect Shuffle (Combinatorics) (Summer 2019)
Signed Symmetric Chromatic Polynomial (Graph Theory) (Summer 2018)