Or Litany

Or Litany
orlitany at gmail dot com

I am a post-doctoral researcher at FAIR, and Stanford University; working under Prof. Jitendra Malik and Prof. Leonidas Guibas. Previously, I was a post-doc at the Technion and a research intern at Microsoft, Intel and Google. I received my PhD from Tel-Aviv University, where I was advised by Prof. Alex Bronstein. I received my B.Sc. in Physics and Mathematics from the Hebrew University under the auspices of “Talpiot”.

My research interests include: 3D deep learning, computational shape analysis and representation learning.

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News

July 22: Our paper Deep Hough Voting for 3D Object Detection in Point Clouds Was accepted as an oral presentation at ICCV 2019.

July 12: Our paper Dual-primal graph convolutional networks Was accepted to GEM workshop

April 25: I was invited as a speaker at the Deep Learning for Science Summer School

April 23: Our paper SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels just won Best paper award at ICLR workshop on Learning from Limited Labeled Data

April 22: Check out our new paper on Object detection in 3D point clouds

Our paper "Self-supervised Learning of Dense Shape Correspondence" was accepted as an oral presentation at CVPR 2019!

We are organizing a tutorial on "Functional Maps: A Flexible Representation for Learning and Computing Correspondence" at ECCV 2018 in Munich. See you there!

Invited talks

Geometric Deep Learning: Introduction
Deep Learning for Science School
slides / video

Deep Learning for “Exotic” Data Like 3D Meshes and Point-Clouds
San Francisco Deep Learning Meetup
slides / video

Deep Hough Voting for 3D object detection in point clouds
Xerox PARC, NYU, Cornell-Tech
slides
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
ICLR LLD workshop, New Orleans
slides / talk

Workshops / Tutorials

Deep Learning for Computer Graphics and Geometry Processing
With: Niloy J. Mitra, Iasonas Kokkinos, Leonidas Guibas, Michael Bronstein, Emanuele Rodolà, Federico Monti
Tutorial at Eurographics 2019
Functional Maps: A Flexible Representation for Learning and Computing Correspondence
With: Maks Ovsjanikov, Emanuele Rodolà, Leonidas Guibas
Tutorial at ECCV 2018
Deep Learning and Geometry With: Ron Kimmel, Emanuele Rodolà, Michael Bronstein, Alex Bronstein
Workshop at EUSIPCO 2017

Publications

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