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Internships: I am always happy to host and mentor PhD and motivated MSc students at Adobe, San Francisco (typically over the summer). If you would like to work with me, please send me an email describing your past experience and current research interests. Make sure to include your CV, 1 or 2 references and a paragraph about specific topics you would like to explore during your internship.

In the past, I have had the pleasure to collaborate with the following students: Julien Philip, Dmitriy Smirnov, Utkarsh Singhal, Tamar Rott Shaham, Zhihao Xia, Spandan Madan, Ishit Mehta, Mustafa Işık, Pradyumna Reddy.

Bio: I am currently a Research Scientist at Adobe. I was previously a PhD student at MIT CSAIL, where I had the chance to work under the supervision of Prof. Frédo Durand. My research interests include computational photography, computer vision and machine learning. Prior to joining MIT, I completed my undergraduate studies in France, at École Polytechnique, with a focus on Applied Mathematics.

If you are looking for the economist, that would be my sister Sarah.


  • Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
    Julien Philip, Sébastien Morgenthaler, Michaël Gharbi, George Drettakis
    ToG 2021

  • Interactive Monte Carlo Denoising using Affinity of Neural Features
    Mustafa Işık, Krishna Mullia, Matthew Fisher, Jonathan Eisenmann, Michaël Gharbi
    Siggraph 2021

  • MarioNette: Self-Supervised Sprite Learning
    Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin Solomon
    NeurIPS 2021

  • Modulated Periodic Activations for Generalizable Local Functional Representations
    Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, Manmohan Chandraker
    ICCV 2021

  • Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
    James Hong, Matthew Fisher, Michaël Gharbi, Kayvon Fatahalian
    ICCV 2021

  • Im2Vec: Synthesizing Vector Graphics without Vector Supervision
    Pradyumna Reddy, Michaël Gharbi, Michal Lukáč, Niloy J. Mitra
    CVPR 2021

  • Spatially-Adaptive Pixelwise Networks for Fast Image Translation
    Tamar Rott Shaham, Michaël Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli
    CVPR 2021

  • Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
    Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
    CVPR 2021

  • Differentiable Vector Graphics Rasterization for Editing and Learning
    Tzu-Mao Li, Michal Lukáč, Michaël Gharbi, Jonathan Ragan-Kelley
    Siggraph Asia 2020

  • Basis Prediction Networks for Effective Burst Denoising with Large Kernels
    Zhihao Xia, Federico Perazzi, Michaël Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti
    CVPR 2020

  • A Dataset of Multi-Illumination Images in the Wild
    Lukas Murmann, Michaël Gharbi, Miika Aittala, Frédo Durand
    ICCV 2019

  • Sample-based Monte Carlo Denoising using a Kernel-Splatting Network
    Michaël Gharbi, Tzu-Mao Li, Miika Aittala, Jaakko Lehtinen, Frédo Durand
    Siggraph 2019

  • Multi-view Relighting using a Geometry-Aware Network
    Julien Philip, Michaël Gharbi, Tinghui Zhou, Alexei A. Efros, George Drettakis
    Siggraph 2019

  • Learning to Optimize Halide with Tree Search and Random Programs
    Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley
    Siggraph 2019

  • Learning Efficient Image Processing Pipelines
    Michaël Gharbi
    MIT PhD Thesis

  • Differentiable Programming for Image Processing and Deep Learning in Halide
    Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand. Jonathan Ragan-Kelley,
    Siggraph 2018

  • Convolutional Neural Network for Earthquake Detection and Location
    Thibaut Perol, Michaël Gharbi, Marine Denolle
    Science Advances 2018

  • Deep Bilateral Learning for Real-Time Image Enhancement
    Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand
    Siggraph 2017

  • Deep Joint Demosaicking and Denoising
    Michaël Gharbi, Gaurav Chaurasia, Sylvain Paris, Frédo Durand
    Siggraph Asia 2016

  • Transform Recipes for Efficient Cloud Photo Enhancement
    Michaël Gharbi, YiChang Shih, Gaurav Chaurasia, Jonathan Ragan-Kelley, Sylvain Paris, Frédo Durand
    Siggraph Asia 2015

  • A Gaussian Approximation of Feature Space for Fast Image Similarity
    Michaël Gharbi, Tomasz Malisiewicz, Sylvain Paris, Frédo Durand
    MIT Technical Report 2012