Ines Chami

  • Ph.D. Candidate @Stanford University
  • Department: ICME
  • Email:

I recently graduated from my Ph.D. in ICME at Stanford University where I was advised by Prof. Christopher Ré. Prior to attending Stanford, I studied Mathematics and Computer Science at Ecole Centrale Paris.

My research interests include Representation Learning and non-Euclidean geometries (e.g., hyperbolic geometry). More specifically, I am interested in designing embedding models that can learn representations for complex relational structures such as graphs. I am particularly excited by applications in the field of Natural Language Processing, such as linking entities in Knowledge Graphs. During my studies, I had the chance to work on Question Answering at Microsoft AI and Research in 2017, and also spent the Summer of 2018 at Google Research, where I worked on graph-based representation learning.

During my free time, I enjoy surfing, practicing yoga and photography. I posted some of my pictures in the Photography section.

Keywords: Graph Representation Learning, Non-Euclidean Geometry, Knowledge Graphs



Hyperbolic Dimensionality Reduction via Horospherical Projections
International Conference on Machine Learning (ICML), 2021.
Ines Chami*, Albert Gu*, Dat Nguyen* and Christopher Ré.
[pdf] [code]

Tree Covers: An Alternative to Metric Embeddings
Differential Geometry meets Deep Learning Workshop @NeurIPS, 2020.
Roshni Sahoo, Ines Chami and Christopher Ré.

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Ines Chami, Albert Gu, Vaggos Chatziafratis and Christopher Ré.
[pdf] [code] [video]

Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Arxiv preprint, 2020.
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré and Kevin Murphy.
[pdf] [Tensorflow code]

Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Annual Conference of the Association for Computational Linguistics (ACL), 2020. [pdf]
Graph Representation Learning Workshop @NeurIPS, 2019. [pdf]
Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi and Christopher Ré.
[Tensorflow code] [PyTorch code] [video]

Hyperbolic Graph Convolutional Neural Networks
Advances in Neural Information Processing Systems (NeurIPS), 2019.
Ines Chami*, Rex Ying*, Christopher Ré and Jure Leskovec.
[pdf] [code] [website]

Referring Relationships
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Ranjay Krishna*, Ines Chami*, Michael Bernstein and Li Fei-Fei.
[pdf] [code] [website] [video]

Abstract Meta Concept Features for Text-Illustration
ACM International Conference on Multimedia Retrieval (ICMR), 2017. (Oral Presentation)
Ines Chami*, Youssef Tamaaazousti* and Hervé Le Borne.
[pdf] [slides] [poster]

Image Annotation and Two Paths to Text-Illustration
CLEF (Working Notes), 2016.
Hervé Le Borne, Etienne Gadeski, Ines Chami, Thi Quynh Nhi Tran, Youssef Tamaaazousti, Alexandru Lucian Ginsca and Adrian Popescu.



Into the Wild: Machine Learning In Non-Euclidean Spaces by Frederic Sala, Ines Chami, Adva Wolf, Albert Gu, Beliz Gunel and Christopher Ré. October 2019.

Massive Multi-Task Learning with Snorkel MeTaL: Bringing More Supervision to Bear by Braden Hancock, Clara McCreery, Ines Chami, Vincent S. Chen, Sen Wu, Jared Dunnmon, Paroma Varma, Max Lam and Christopher Ré. March 2019.


Antelope Canyon, Arizona, USA, 2018

Antelope Canyon, Arizona, USA, 2018

Salar de Uyuni, Bolivia, 2014

Salar de Uyuni, Bolivia, 2014

Salar de Uyuni, Bolivia, 2014

Salar de Uyuni, Bolivia, 2014

Salvador de Bahia, Brazil, 2015

Chapada Diamantina, Brazil, 2015

Chapada Diamantina, Brazil, 2015

Praia Lopez Mendez, Ihla Grande, Brazil, 2015

Lisboa, Portugal, 2014

Lisboa, Portugal, 2014

Taghazout, Morocco, 2016

Santa Teresa, Costa Rica, 2016

Essaouira, Morocco, 2016

Essaouira, Morocco, 2016

Tafedna, Morocco, 2016

Marrakech, Morocco, 2016