Aude Oliva

Aude Oliva 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.

Selected Publications

• Huang, I., Lin, W., Mirza, M. J., Hansen, J. A., Doveh, S., Butoi, V. I., Herzig, R., Arbelle, A., Kuehne, H., Darrell, T., Gan, C., Oliva, A., Feris, R., & Karlinsky, L. (2024). ConMe: Rethinking evaluation of compositional reasoning for modern VLMs. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.
• Wang, R., Ghosh, S., Cox, D., Antognini, D., Oliva, A., Feris, R., & Karlinsky, L. (2024). Trans-LoRA: Towards data-free transferable parameter efficient finetuning. In Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024) (Article 1957, pp. 61217–61237).
• Lahner, B., Dwivedi, K., Iamshchinina, P., Graumann, M., Lascelles, A., Roig, G., Gifford, A. T., Pan, B., Jin, S. Y., Ratan Murty, N. A., Kay, K., Olivetti, A., & Cichy, R. (2024). Modeling short visual events through the BOLD Moments video fMRI dataset and metadata. Nature Communications, 15(1), Article 6241.

Media

• November 7, 2024: IBM Research, Serving customized AI models at scale with LoRA
• July 24, 2024: IBM Research, What’s an LLM context window and why is it getting larger?
• June 12, 2024: MIT News, Researchers use large language models to help robots navigate