Samar Khanna

I am an ML engineer at Aurora, working on long-range perception for autonomous trucks.

Recently, I received my MS in CS with a distinction in research from Stanford University, specializing in AI. I worked with Prof. Stefano Ermon on self-supervised learning, generative (diffusion) models, and on building foundation models for geospatial tasks. My research interests center on making ML models generalize effectively to new, real-world data, especially to solve societally relevant tasks such as sustainability.

Before Stanford, I completed my Bachelor’s in CS at Cornell University, where I did research with Dean Kavita Bala and Prof. Bharath Hariharan. I was also actively involved as a team-lead in Cornell Data Science, a student-run project team.

Professionally, I have completed internships at NVIDIA and at Uber ATG (twice), where I was given a chance to hone my real-world ML engineering skills.

For fun, I like to swim and play tennis– to relax, I enjoy reading books and watching movies.

Announcements

Jun 18, 2023	I graduated from Stanford!
May 29, 2021	4 years of Cornell all wrapped up…

Selected Publications

DiffusionSat: A Generative Foundation Model for Satellite Imagery

Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, and Stefano Ermon

In The Twelfth International Conference on Learning Representations , 2024

arXiv Bib Code Website

@inproceedings{diffusionsat2024,
  title = {DiffusionSat: A Generative Foundation Model for Satellite Imagery},
  author = {Khanna, Samar and Liu, Patrick and Zhou, Linqi and Meng, Chenlin and Rombach, Robin and Burke, Marshall and Lobell, David B. and Ermon, Stefano},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year = {2024},
  url = {https://openreview.net/forum?id=I5webNFDgQ},
}

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

Yezhen Cong*, Samar Khanna*, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, and Stefano Ermon

In Advances in Neural Information Processing Systems , 2022

arXiv Bib Code Website

@inproceedings{satmae2022,
  title = {Sat{MAE}: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery},
  author = {Cong*, Yezhen and Khanna*, Samar and Meng, Chenlin and Liu, Patrick and Rozi, Erik and He, Yutong and Burke, Marshall and Lobell, David B. and Ermon, Stefano},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Oh, Alice H. and Agarwal, Alekh and Belgrave, Danielle and Cho, Kyunghyun},
  year = {2022},
  url = {https://openreview.net/forum?id=WBhqzpF6KYH},
}

GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David B. Lobell, and Stefano Ermon

In The Twelfth International Conference on Learning Representations , 2024

arXiv Bib

@inproceedings{geollm2024,
  title = {Geo{LLM}: Extracting Geospatial Knowledge from Large Language Models},
  author = {Manvi, Rohin and Khanna, Samar and Mai, Gengchen and Burke, Marshall and Lobell, David B. and Ermon, Stefano},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year = {2024},
  url = {https://openreview.net/forum?id=TqL2xBwXP3},
}

Denoising Diffusion Bridge Models

Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon

In The Twelfth International Conference on Learning Representations , 2024

arXiv Bib Code

@inproceedings{ddbm2024,
  title = {Denoising Diffusion Bridge Models},
  author = {Zhou, Linqi and Lou, Aaron and Khanna, Samar and Ermon, Stefano},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year = {2024},
  url = {https://openreview.net/forum?id=FKksTayvGo},
}

Large Language Models are Geographically Biased

Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, and Stefano Ermon

arXiv preprint arXiv:2402.02680, 2024

arXiv Bib

@article{llmbias2024,
  title = {Large Language Models are Geographically Biased},
  author = {Manvi, Rohin and Khanna, Samar and Burke, Marshall and Lobell, David and Ermon, Stefano},
  journal = {arXiv preprint arXiv:2402.02680},
  year = {2024},
}

Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting

Rylan Schaeffer*, Kateryna Pistunova*, Samar Khanna*, Sarthak Consul*, and Sanmi Koyejo

In ICML 2023 Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning , 2023

arXiv Bib

@inproceedings{invalid2023,
  title = {Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting},
  author = {Schaeffer*, Rylan and Pistunova*, Kateryna and Khanna*, Samar and Consul*, Sarthak and Koyejo, Sanmi},
  booktitle = {ICML 2023 Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning},
  year = {2023},
  url = {https://icml.cc/virtual/2023/27230},
}

Differentiable Weight Masks for Domain Transfer

Samar Khanna*, Skanda Vaidyanath*, and Akash Velu*

In ICCV 2023 Workshop on Out of Distribution Generalization in Computer Vision , 2023

arXiv Bib

@inproceedings{weightmask2023,
  title = {Differentiable Weight Masks for Domain Transfer},
  author = {Khanna*, Samar and Vaidyanath*, Skanda and Velu*, Akash},
  booktitle = {ICCV 2023 Workshop on Out of Distribution Generalization in Computer Vision},
  year = {2023},
  url = {http://www.ood-cv.org/paper.html},
}