Aishwarya Agrawal
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2020
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 is also a core academic member of Mila — Quebec AI Institute. She also spends one day a week as a research scientist at Google DeepMind.
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).
Awards
- Young Alumni Excellence Award for Outstanding Academic Achievements, Indian Institute of Technology, 2023
- AAAI / ACM SIGAI Dissertation Award Runner-up, 2019
- Georgia Tech College of Computing Dissertation Award, 20202
- Georgia Tech Sigma Xi Best Ph.D. Thesis Award, 2020
- Google Fellowship 2019-2020 (declined)
- Facebook Fellowship 2019-2020 (declined)
- NVIDIA Graduate Fellowship, 2018-2019
- Rising Star in EECS, 2018
Relevant Publications
- Ahmadi, S., & Agrawal, A. (2024). An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics. In Findings of the Association for Computational Linguistics: EACL.
- Manas, O., Krojer, B., & Agrawal, A. (2024). Improving Automatic VQA Evaluation Using Large Language Models. In the 38th Annual AAAI Conference on Artificial Intelligence.
- Zhang, L., Wu, Y., Mo, F., Nie, J. Y., & Agrawal, A. (2023). MoqaGPT: Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model. In the Findings of the Association for Computational Linguistics (EMNLP).
- Bugliarello, E., Sartran, L., Agrawal, A., Hendricks, L. A., & Nematzadeh, A. (2023) Measuring Progress in Fine-grained Vision-and-Language Understanding. In the Association for Computational Linguistics (ACL).
- Manas, O., Rodrguez, P., Ahmadi, S., Nematzadeh, A., Goyal, Y., & Agrawal, A. (2023) MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting. Published in the European Chapter of the Association for Computational Linguistics (EACL).