Samar Khanna

MS CS @ Stanford | CS @ Cornell | ML @ Aurora

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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

  1. diffusion_sat_main.png
    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
  2. sat_mae.png
    SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
    Samar Khanna*, Yezhen Cong*, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, and Stefano Ermon
    In Advances in Neural Information Processing Systems , 2022
  3. geollm_teaser.png
    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
  4. ddbm.png
    Denoising Diffusion Bridge Models
    Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon
    In The Twelfth International Conference on Learning Representations , 2024
  5. llm_bias.jpeg
    Large Language Models are Geographically Biased
    Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, and Stefano Ermon
    In Forty-first International Conference on Machine Learning , 2024