I got interested in Computer Vision while trying to make simple robots autonomous during my undergraduate degree. During that time, I also wrote open-source image-processing modules for SimpleCV and scikit-image. My interests led me to my Master's degree at NYU where I initially worked on wavelet-based convolutions for detecting reflection symmetry in images. I started getting interested in deep-learning based methods when I used them to analyze ultrasound images of the human heart. While at NYU, I also worked on unsupervised learning methods for video, by automatically disentangling content and pose from video frames. Before coming to Google, I was working at FeatureX, where I used deep-neural networks to solve a variety of computer vision problems for satellite images. As a researcher, I like to ask a lot of why and how questions. As of now, my biggest fascination is the loss landscape of deep-neural networks and how SGD manages to traverse it. My current research focuses on finding curricula to train neural networks faster. During my residency, I have repeatedly been impressed by the amount of compute and learning resources at Google. I especially like the research environment here because it encourages me to tackle ambitious problems. I am a huge fan of open-source software and the Python programming language. I love action and science-fiction movies, particularly, how sometimes they can inspire real-world inventions.