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I received my PhD from Stanford University supported by a Hertz Fellowship where I worked with Pat Hanrahan in the Graphics Lab. I completed my undergraduate degree at the California Institute of Technology working with Mathieu Desbrun. During a postdoc at Stanford, I have also worked on the data analytics team at Khan Academy.
My research focuses on combining computer graphics, vision, and machine learning to make it faster and more fun to complete creative tasks.
   

Technology

I've helped developed several tools that have shipped in many Adobe products.
Illustrator 2020

This technology lets you automatically recolor vector artwork to match the colors of a target photograph or palette.

Photoshop 2020 Neural Filters and Photoshop Elements 2019

This tool uses a generative deep network to automatically colorize black and white photographs and can optionally incorporate user-guided coloring suggestions.

Illustrator 2018 and Illustrator for iPad 2020

The freeform gradients tool naturally lets you control the diffusion of colors across your vector graphics and is one of many ways we are looking into making the Gradient Mesh tool easier and faster to use.

Dimension 2019

The renderer in Adobe Dimension was updated to use a deep network that efficiently reduces the image noise due to Monte-carlo sampling in its photorealistic renderer.

Illustrator 2019

Often, your design contains multiple copies of similar objects, such as logos. If there is a need to make an edit to all such objects, you can use the global editing tool to edit all similar objects in the design in one step.

Illustrator 2017

Puppet Warp lets you twist and distort parts of your artwork, such that the transformations appear natural. You can add, move, and rotate pins to seamlessly transform your artwork into different variations using the Puppet Warp tool in Illustrator.

Dimension 2017

Using deep learning, we can automatically estimate the perspective a photograph was taken from, making it much easier to composite 3D content into those scenes. This is an application of our CVPR 2017 paper on Deep Single Image Camera Calibration.