About Me

I'm a first-year PhD student at MIT, advised by Phillip Isola. I am supported by the NSF Graduate Research Fellowship and an MIT HDTV Fellowship. I am broadly interested in generalization, generative models, and representation learning, particularly in computer vision. Lately I've been working on datapoint selection, and using generative models as data sources for benchmarking & representation learning.

Previously I got my bachelors in Computer Science and in Mathematics at MIT, working at the MIT Center for Brains, Minds, and Machines with Pawan Sinha and Xavier Boix. In the past I've also been fortunate to intern at Deepmind (large language models), D. E. Shaw (reinforcement learning research), Apple (applied machine learning), and Two Sigma (software engineering). In my free time I enjoy hiking, writing, and running.

Research

	Recurrent connections facilitate symmetry perception in deep networks Shobhita Sundaram, Darius Sinha, Matthew Groth, Tomotake Sasaki, Xavier Boix Scientific Reports, 2022. ICLR Generalization Beyond the Training Distribution in Brains and Machines Workshop, 2021. Paper / Poster / Code
	GAN-based Data Augmentation for Chest X-ray Classification Shobhita Sundaram, Neha Hulkund KDD Applied Data Science for Healthcare Workshop, 2021 Paper
	Do Neural Networks for Segmentation Understand Insideness? Kimberly Villalobos, Vilim Stih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, Xavier Boix Neural Computation*, 2021 Paper / Code