About Me

I'm a third-year PhD student at MIT, advised by Phillip Isola. I also collaborate with Yonglong Tian and Sara Beery.

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 representation learning?
  2. Human-aligned representations: How can we better align representations with human perception and perceptual preferences?
  3. Science of training data: How can we better understand, improve, and sample from training datasets to enable more efficient learning and scaling?

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

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When Does Perceptual Alignment Benefit Vision Representations?
Shobhita Sundaram*, Stephanie Fu*, Lukas Muttenthaler, Netanel Tamir, Lucy Chai, Simon Kornblith, Trevor Darrell, Phillip Isola.
NeurIPS, 2024.
Paper / Website / Code
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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 / Website / Code
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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
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GAN-based Data Augmentation for Chest X-ray Classification
Shobhita Sundaram*, Neha Hulkund*
Workshop on Applied Data Science for Healthcare, KDD 2021

Paper

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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

Service

Other Writing