Ines Chami

  • Ph.D. Candidate @Stanford
  • Department: ICME
  • Email: chami@stanford.edu

I am a Ph.D. candidate in ICME at Stanford University where I am advised by Professor Chris Ré. Prior to attending Stanford, I studied Mathematics and Computer Science at Ecole Centrale Paris.

My research interests include Machine Learning, Representation Learning, Deep learning and Relational Reasoning. More specifically, I am interested in designing models that can learn representations for complex relational structures such as graphs. I am particularly excited about understanding how non-Euclidean geometries (e.g., hyperbolic geometry), can lead to more expressive representations for some types of relational structures. I am also excited by applications in the field of Computer Vision and Natural Language Processing, such as understanding how objects interact in images or how entities are related 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 Semi-Supervised Learning.

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

Keywords: Machine Learning, Deep Learning, Graph Representation Learning, Non-Euclidean Geometry, Computer Vision, Natural Language Processing

News

  • [10/19]: Check out our new blog about Machine Learning in non-Euclidean spaces.
  • [09/19]: Excited to join Sujith Ravi's team at Google for a Fall internship!
  • [08/19]: Our paper on Hyperbolic Graph Convolutional Neural Networks is accepted at NeurIPS 2019!
  • [07/19]: I'm really honored to have received the Total Innovation Fellowship for the 2019/2020 academic year.
  • [05/19]: Our team achieved state-of-the-art performance on the MMTL GLUE benchmark! Check out the corresponding blog post here.

Publications

Low-Dimensional Knowledge Graph Embeddings via Hyperbolic Rotations
Graph Representation Learning Workshop (NeurIPS), 2019.
Ines Chami, Adva Wolf, Frederic Sala and Christopher Ré.
[pdf]

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.
[pdf]

Blog

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.

Awards

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