I am a second year Ph.D. student in the Computation and Neural Systems program at the California Institute of Technology. I am co-advised by Professors Pietro Perona and Yisong Yue. Before coming to Caltech, I received my B.S. in physics from the University of Minnesota, Twin Cities.

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My academic interests are at the intersection of machine learning and neuroscience, with the end goal toward developing machine intelligence, while drawing inspiration from biology where appropriate. My current research is centered on deep learning in computer vision.



October 2016.

Thoughts on Generative Models

Generative deep neural networks, combined with probabilistic models, have been recently showing promising capabilities, hinting at the possiblilty of extracting meaningful abstract representations in unsupervised ways.

June 2016.

GoogLeNet in Keras

In this blog post, I'll show you how I implemented GoogLeNet in Keras and copied over the weights from Caffe. Then we'll classify some cats!

March 2016.

Implementing Backpropagation

Backpropagation is a method for computing derivatives in artificial neural networks, allowing us to use gradient descent to train these models. Here, I walk through implementing backpropagation.