About Me

I'm a second-year PhD student at MIT, advised by Phillip Isola. 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, representation learning, and generalization, 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.


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 (spotlight), 2023.
Paper / Website / Code
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


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


Facilitating Fairness in Transfer Learning through Distributionally Robust Finetuning

Final project for 6.864: Natural Language Processing, 2021

Extension of a Bayesian Hierarchical Model for Moral Judgments

Final project for 6.804: Computational Cognitive Science, 2020

Computational Reading of Gender in Novels (1770-1922)

Research conducted in the MIT Digital Humanities Lab, 2018

Read about our findings in Ms. Magazine


blind-date MIT Science Policy Review, Associate Editor
clean-usnob MIT Chroma: "Rebuilding Despite the Risks"