About
Appointed Canada CIFAR AI Chair – 2024
Shuang Li is an Assistant Professor at the University of Toronto and Faculty Member at the Vector Institute.
Li’s particular expertise is on incorporating compositional AI systems into deep neural networks, thereby enhancing their ability to generalize and solve novel and complex tasks, such as generating images and videos based on complicated specifications, or enabling robots to perform a diverse range of tasks. Her work has many potential applications across fields including biology, robotics and art production.
Awards
- Facebook Research Fellowship, Meta, 2021
- Outstanding Paper Award, NeurIPS Controllable Generative Modeling Workshop, 2021
- Best Paper Award, NeurIPS Cooperative AI Workshop, 2020
- Adobe Research Fellowship, 2019
- Seneff-Zue CS Fellowship, MIT, 2018
Relevant Publications
- Li, S., Du, Y., Tenenbaum, J. B., Torralba, A., & Mordatch, I. (2023). “Composing Ensembles of Pre-trained Models via Iterative Consensus.” International Conference on Learning Representations (ICLR).
- Li, S., Puig, X., Paxton, C., Du, Y., Wang, C., Fan, L., Chen, T., Huang, D.-A., Akyürek, E., Anandkumar, A., Andreas, J., Mordatch, I., Torralba, A., & Zhu, Y. (2022). “Pre-Trained Language Models for Interactive Decision-Making.” Advances in Neural Information Processing Systems (NeurIPS) Oral.
- Liu, N., Li, S., Du, Y., Torralba, A., & Tenenbaum, J. B. (2022). “Compositional Visual Generation with Composable Diffusion Models.” European Conference on Computer Vision (ECCV).
- Li, S., Du, Y., van de Ven, G. M., & Mordatch, I. (2022). “Energy-Based Models for Continual Learning.” Conference on Lifelong Learning Agents (CoLLAs) Oral.
- Li, Y., Li, S., Sitzmann, V., Agrawal, P., & Torralba, A. (2021). “3D Neural Scene Representations for Visuomotor Control.” Conference on Robot Learning (CoRL) Oral.