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
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]
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]
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]
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.