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
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.