About Me

I'm a second-year PhD student at MIT, advised by Phillip Isola. I also collaborate with Yonglong Tian as a Student Researcher at Google Research.

I am supported by the NSF Graduate Research Fellowship, and previously by the MIT HDTV Grand Alliance Fellowship. I am broadly interested in generative models and representation learning, particularly data-centric approches. At the moment I'm working on the following questions:

1. Learning from synthetic data: How can we harness generative models as a data source for visual representation learning? How do we generate "good" synthetic data?
2. Human-aligned vision: How can we better align visual representations with human perception and perceptual preferences?
3. Data-efficient learning: How can we sample and learn from data (real or synthetic) more efficiently?

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, running, and tennis.

Publications

* indicates equal contribution

	DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data Stephanie Fu*, Netanel Tamir*, Shobhita Sundaram*, Lucy Chai, Richard Zhang, Tali Dekel, Phillip Isola. NeurIPS, 2023 (spotlight). Paper / Code / Website
	Recurrent connections facilitate symmetry perception in deep networks Shobhita Sundaram*, Darius Sinha*, Matthew Groth, Tomotake Sasaki, Xavier Boix Scientific Reports, 2022. Workshop on Generalization Beyond the Training Distribution in Brains and Machines, ICLR 2021. Paper / Code / Poster
	GAN-based Data Augmentation for Chest X-ray Classification Shobhita Sundaram*, Neha Hulkund* Workshop on Applied Data Science for Healthcare, KDD 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

Invited Talks

DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data Adobe GenTech Seminar, October 2023

DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data Voxel51 Computer Vision Meetup, July 2023

Other Writing

MIT Science Policy review (Associate Editor)
MIT Chroma: "Rebuilding Despite the Risks"