Aishwarya Agrawal is a Canada CIFAR AI Chair and an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université Montréal. She also works as a research scientist at DeepMind’s Montréal office.
Her research interests lie at the intersection of computer vision, deep learning and natural language processing, with a focus on developing artificial intelligence (AI) systems that can ‘see’ (i.e. understand the contents of an image: who, what, where, doing what?) and ‘talk’ (ie communicate the understanding to humans in free-form natural language).
- NVIDIA Graduate Fellowship, 2018
- Rising Star in EECS, 2018
- Best Poster Award, Object Understanding for Interaction Workshop, International Conference on Computer Vision, 2015
Agrawal, A., Batra, D., Parikh, D., & Kembhavi, A. (2018). Don’t just assume; look and answer: Overcoming priors for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4971-4980).
Ramakrishnan, S., Agrawal, A., & Lee, S. (2018). Overcoming language priors in visual question answering with adversarial regularization.
Agrawal, A., Kembhavi, A., Batra, D., & Parikh, D. (2017). C-vqa: A compositional split of the visual question answering (vqa) v1. 0 dataset.
Agrawal, A., Batra, D., & Parikh, D. (2016). Analyzing the behavior of visual question answering models.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (pp. 2425-2433).
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.