Sachit Menon

I am a PhD student in Computer Science at Columbia University working on problems in machine learning. My doctoral work is supported by a Columbia Presidential Fellowship and an NSF Graduate Research Fellowship.

Extremely important: I am also an expert on large dogs, despite a lack of recognition on the topic.

Previously, I completed by B.S. in Mathematics and Computer Science at Duke University, where I was fortunate to work with Dr. Cynthia Rudin.

Email  /  CV  /  Google Scholar

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Through my research, I hope to develop new ways to learn or utilize models at scale. This makes me particularly interested in representation learning, generative modeling, and self-supervised methods, as well as their intersection. Recently, I am particularly interested in the potential for language to aid vision tasks.

Selected papers:
Visual Classification via Description from Large Language Models
Sachit Menon, Carl Vondrick.
ICLR 2023, Notable - Top 5% (Oral).
arXiv, Code

We enhance zero-shot recognition with vision-language models by comparing to category descriptors from GPT-3, enabling better performance in an interpretable setting that also allows for incorporation of new concepts and bias mitigation.

Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse
Sachit Menon, David Blei, Carl Vondrick.
UAI 2022.

We incorporate a `critic' into the standard VAE framework that aims to pair up corresponding samples from the observed and latent distributions, mitigating posterior collapse.

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Sachit Menon*, Alex Damian*, Shijia Hu, Nikhil Ravi, and Cynthia Rudin.
CVPR, 2020

Self-supervised search of the outputs of a generative model, leveraging some properties of high-dimensional Gaussians, enables super-resolution with higher perceptual quality than previous methods.


TA, Neural Networks and Deep Learning with Prof. Rich Zemel, Columbia University
TA, Machine Learning (Graduate) with Prof. Cynthia Rudin , Duke University


Organizer, Learning from Unlabeled Video Workshop (LUV 2021), CVPR 2021

Website template credits to Jon Barron.