About me

I'm a 3rd year Ph.D. student at Cornell Tech in NYC, advised by Prof. Volodymyr Kuleshov. My research focuses on Generative AI for text and images.

Projects

Select Papers

  • Subham S. Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov. Simple and Effective Masked Diffusion Language Models. Under Review, 2024. [paper, code, project]


    Subham S. Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov. Diffusion Models With Learned Adaptive Noise. Under Review, 2024. [paper, code, project]


    Subham S. Sahoo*, Anselm Paulus*, Marin Vlastelica, Vit Musil, Volodymyr Kuleshov, Georg Martius. Backpropagation through Combinatorial Algorithms: Identity with Projection Works. International Conference on Learning Representations (ICLR - 2023), 2023. [paper, code]


    Subham S. Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley. Scaling Symbolic Methods using Gradients for Neural Model Explanation. International Conference on Learning Representations (ICLR - 2021), 2021. [paper, code]


    Subham S. Sahoo, Christoph H. Lampert, Georg Martius. Learning Equations for Extrapolation and Control. International Conference on Machine Learning (ICML - 2018), 2018. [paper, project, code]

Daniel lewis
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Daniel lewis

Richard was hired to create a corporate identity. We were very pleased with the work done. She has a lot of experience and is very concerned about the needs of client. Lorem ipsum dolor sit amet, ullamcous cididt consectetur adipiscing elit, seds do et eiusmod tempor incididunt ut laborels dolore magnarels alia.

Background

Education

  1. Cornell Tech, New York, USA.

    2022 — present

    Ph.D in Computer Science.
    Thesis: Diffusion Models for Discrete Structures.
    Committee: Prof. Volodymyr Kuleshov (chair), Prof. Noah Snavely, Prof. Bart Selman.

  2. Indian Institute of Technology - Kharagpur, India.

    2015 — 2019

    Bachelor's in Electrical Engineering.

Experience

  1. Cruise, San Francisco, USA.

    2023 (May - July)

    Research intern.
    Team: AV Behaviors.

  2. Max Planck Institute for Intelligent Systems, Tubingen, Germany.

    2021 (Aug - Dec)

    Visiting Researcher.
    Team: Autonomous Learning Group.

  3. Google Research, Mountain View, USA.

    2019 — 2021

    AI Resident.
    Teams: Accelerated Science, Operations Research.

Papers

  • Subham S. Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov. Simple and Effective Masked Diffusion Language Models. Under Review, 2024. [paper, code, project]


    Subham S. Sahoo, John X. Morris, Aaron Gokaslan, Vitaly Shamtikov, Volodymyr Kuleshov. Gradient-Free Classifier-Based Guidance for Diffusion Models. Under Review, 2024.


    Subham S. Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov. Diffusion Models With Learned Adaptive Noise. Under Review, 2024. [paper, code, project]

  • Subham S. Sahoo, Anselm Paulus, Marin Vlastelica, Vit Musil, Volodymyr Kuleshov, Georg Martius. Backpropagation through Combinatorial Algorithms: Identity with Projection Works. International Conference on Learning Representations (ICLR - 2023), 2023. [paper, code]


    Phillip Si, Zeyi Chen, Subham S. Sahoo, Subham S. Sahoo, Yair Schiff, Volodymyr Kuleshov. Semi-Autoregressive Energy Flows: Towards Determinant-Free Training of Normalizing Flows. nternational Conference on Machine Learning (ICML - 2023), 2023. [paper]

  • Subham S. Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley. Scaling Symbolic Methods using Gradients for Neural Model Explanation. International Conference on Learning Representations (ICLR - 2021), 2021. [paper, code]


    Subham S. Sahoo, Ross Anderson, Christian Tjandraatmadja. Local Search on TPUs. pre-print, 2021. [paper]

  • Subham S. Sahoo. Training Neual Networks using SAT solvers. pre-print, 2018. [paper]


    Subham S. Sahoo, Christoph H. Lampert, Georg Martius. Learning Equations for Extrapolation and Control. International Conference on Machine Learning (ICML - 2018), 2018. [paper, project, code]

Projects