Portrait

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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. Fr├ędo Durand. My research interests include computational photography, computer vision and machine learning. Prior to joining MIT, I completed my undergraduate studies in France, at Ecole Polytechnique, with a focus on Applied Mathematics.

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


Research

  • 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 (to appear)

  • 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