MIT Director, MIT-IBM Watson AI Lab; Director of Strategic Industry Engagement, MIT Schwarzman College of Computing
Aude Oliva, PhD is the MIT director in the MIT-IBM Watson AI Lab and director of strategic industry engagement in the MIT Schwarzman College of Computing, leading collaborations with industry to translate natural and artificial intelligence research into tools for the wider world. She is also a senior research scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where she heads the Computational Perception and Cognition group.
Oliva has received an NSF Career Award in computational neuroscience, a Guggenheim fellowship in computer science and a Vannevar Bush Faculty Fellowship in cognitive neuroscience. She has served as an expert to the NSF Directorate of Computer and Information Science and Engineering on the topic of human and artificial intelligence. She is currently a member of the scientific advisory board for the Allen Institute for Artificial Intelligence. Her research is cross-disciplinary, spanning human perception and cognition, computer vision and cognitive neuroscience, and focuses on research questions at the intersection of all three domains. She earned a MS and PhD in cognitive science from the Institut National Polytechnique de Grenoble, France.
- Cascante-Bonilla, P., Shehada, K., Smith, J.S., Doveh, S., Kim, D., Panda, R., Varol, G., Oliva A., Ordonez, V., Feris, R., & Karlinsky, L. (2023). Going Beyond Nouns With Vision & Language Models Using Synthetic Data. International Conference on Computer Vision (ICCV 2023).
- Liu, A. H., Jin, S., Lai, C-I., Rouditchenko, A., Oliva, A., Glass, J.(2022). Cross-Modal Discrete Representation Learning. Association for Computational Linguistics (ACL).
- Kim, Y., Mishra, S., Jin, S., Panda, R., Kuehne, H., Karlinsky, L., Saligrama, V., Saenko, K., Oliva, A., & Feris, R. (2022). How Transferable are Video Representations Based on Synthetic Data? Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track.
- Panda*, R., Chen*, C., Fan, Q., Sun, X., Saenko, K., Oliva, A., & Feris, R. (2021). AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition. International Conference on Computer Vision (ICCV).
- Sun, X., Panda, R., Chen, C., Oliva, A., Feris, R., & Saenko K. (2021). Dynamic Network Quantization for Efficient Video Inference. International Conference on Computer Vision (ICCV).
- Pan, B., Panda, R., Jiang, Y., Wang, Z., Feris, R., Oliva, A. (2021). IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers. Advances in the Neural Information Processing Systems (NeurIPS).
- May 22, 2023: MIT-IBM Watson AI Lab, Creating space for the evolution of generative and trustworthy AI
- November 3, 2022: MIT News, In machine learning, synthetic data can offer real performance improvements
- May 4, 2022: MIT News, Artificial intelligence system learns concepts shared across video, audio, and text
- October 13, 2021: MIT News, Thriving Stars: An initiative to improve gender representation in electrical engineering and computer science.
- August 31, 2020: MIT News, Toward a machine learning model that can reason about everyday actions.
- November 1, 2019: MIT News, What makes an image memorable? Ask a computer.
- September 13, 2018: MIT News, Helping computers fill in the gaps between video frames.
- June 29, 2017: MIT News, Peering into neural networks.