Aude Oliva

Aude Oliva

MIT Director, MIT-IBM Watson AI Lab
Director, MIT Quest Corporate
Program Lead, MIT AI Hardware Program
EECS Industry Officer
Senior Research Scientist, CSAIL
MIT Schwarzman College of Computing

Contact Information:
MIT, 32-D432
MIT, 77 Massachusetts Avenue
Cambridge, MA 02139
Phone: 617 452 2492


After a French baccalaureate in Physics and Mathematics and a B.Sc. in Psychology (minor in Philosophy), Aude Oliva received two M.Sc. degrees –in Experimental Psychology, and in Cognitive Science and a Ph.D from the Institut National Polytechnique of Grenoble, France. She joined the MIT faculty in the Department of Brain and Cognitive Sciences in 2004, the MIT Computer Science and Artificial Intelligence Laboratory - CSAIL - in 2012, and the leadership of the MIT-IBM Watson AI Lab in 2017, and the Quest for Intelligence in 2018. In 2021, she took the leading role of the MIT AI Hardware Program, a new academic-industry partnership between the MIT School of Engineering and the MIT Schwarzman College of Computing. She is also affiliated with the EECS department in the MIT Schwarzman College of Computing and the Athinoula A. Martinos Imaging Center at the McGoven Institute for Brain Research at MIT.

Her research is cross-disciplinary, spanning human perception/cognition, computer vision (visual AI), and cognitive neuroscience, focusing on research questions at the intersection of the three domains. Her work in Computational Perception and Cognition builds on the synergy between human and machine recognition, and how it applies to solving high-level recognition problems like understanding scenes and events, perceiving space, modelling attention, eye movements and memory, as well as predicting subjective properties of images (like memorability). Her research integrates knowledge and tools from computer vision, machine learning, deep neural networks as well as human perception, cognition and neuro-imaging (fMRI, MEG).

Her work is regularly featured in the scientific and popular press, in museums of Art and Science as well as in textbooks of Perception, Cognition, Computer Vision and Design. She is the recipient of a 2006 National Science Foundation CAREER Award in Computational Neuroscience, the 2014 Guggenheim fellowship in Computer Science and the 2016 Vannevar Bush Faculty Fellowship in Cognitive Neuroscience. She is an elected Fellow of the Association for Psychological Science, and an Osher Fellow of the Exploratorium, San Francisco. In 2015-2017, she was appointed as an Expert at the National Science Foundation, Directorate of Computer & Information Science and Engineering (CISE) in the areas of Computational Neuroscience, Human and Artificial intelligence. Her research programs at MIT are funded by the National Science Foundation, the National Security Science and Engineering program, IBM, Facebook, Facebook Reality Lab, and Systems That Learn-CSAIL. She is currently on the Scientific Advisory Board of the Allen Institute for Artificial Intelligence. See her google scholar profile page.

Leadership & Executive Positions

MIT Director, MIT-IBM Watson AI Lab

The MIT-IBM Watson AI Lab is a community of scientists from MIT and IBM Research dedicated to pushing the frontiers of artificial intelligence and translating breakthroughs into real-world impact. Founded in 2017, the Lab works with industry to translate fundamental science into applications that solve immediate problems in the business world and beyond. The Lab currently manages a research portfolio of more than 90 projects, with an emphasis on data-driven, deep learning approaches to understanding language and the visual world and techniques for making large-scale AI systems more efficient and robust. The Lab is also developing AI systems for healthcare and a variety of decision-making applications. In all of its work, the Lab is committed to building trustworthy and socially responsible AI systems.

Director, MIT Quest Corporate

The MIT Quest for Intelligence is advancing research in natural and machine intelligence and driving the development of new tools and technologies for the benefit of society. MIT Quest Corporate is a community of scientists from MIT and industry committed to developing next-generation artificial intelligence tools to solve urgent problems facing business, society and the environment. We are experimenting with ways to transition from AI that performs specialized tasks at super-human levels to more generalized systems that are flexible, fair and transparent. We fund promising ideas that we think can be implemented at scale and create positive and lasting improvements in people’s lives.

Program Lead: MIT AI Hardware Program

The MIT AI Hardware Program is a new academic-industry initiative of the MIT School of Engineering and the MIT Schwarzman College of Computing, dedicated to foster fundamental and use-inspired research towards next-generation Artificial Intelligence hardware. Our overriding goal is innovating technologies that deliver drastically enhanced energy efficiency systems. Our approach involves the entire vertical abstraction stack: materials, devices, circuits, algorithms, software and high-performance computing systems. More in July 2021.

Industry Officer, EECS Alliance

EECS Alliance is a joint initiative between MIT's Department of Electrical Engineering & Computer Science (EECS) and some of the most innovative and impactful companies and organizations in the world. EECS Alliance focuses on achieving four goals: to connect partner companies with MIT EECS students through internships and full time job opportunities; to increase the visibility of partner companies among MIT's EECS students, and to help them highlight the diverse career paths and opportunities available in electrical engineering, computer science, artificial intelligence and decision making; to work with partner companies to build a more equitable, diverse and inclusive future; to revolutionize EECS education and career development by combining the expertise of partner companies, the EECS Department and the MIT ecosystem.

Research Overview

My cross-disciplinary research in Computational Neuroscience, Computational Cognition and Computer Vision, bridges from theory to experiments to applications, accelerating the rate at which discoveries are made by solving problems through a novel way of thinking.

Computational Neuroscience

High-resolution, spatiotemporally resolved neuroimaging is a sort of Holy Grail for neuroscience. It means that we can capture when, where, and in what form information flows through the human brain during mental operations. In the team, we study the fundamental neural mechanisms of human perception and cognition and develop computational models inspired by brain architecture. We are developing state-of-the-art human brain mapping approach fusing magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and computational modeling (CNN), to investigate the neural flow of perceived or imagined events. Unpacking the structure of operations such as sensory perception, memory, imagination, action, and prediction in the human brain has far-reaching implications for understanding not just typical brain functions, but also the maintenance or even augmentation of these functions in the face of internal (disease or injury) and external (information overload) challenges.

Computational Cognition

Understanding cognition on an individual level facilitates communication between natural and artificial systems, resulting in improved interfaces, devices, and neuroprosthetics for healthy and disabled people. Our work has identified that events carry the attribute of memorability, a predictive value of whether a novel event will be later remembered or forgotten. Predicting memorability is not an inexplicable phenomenon: people have a tendency to remember and forget the same images, faces, words, and graphs. Importantly, we are developing computational models that predict what people will remember, as they are encoding an event or even before they witness an event.  Cognitive-level algorithms of memory will be a game changer for society, with applications ranging from accurate medical diagnostic tools to educational materials that will foresee the needs of people, to compensate when cognition fails.

Computer Vision

Inspired by strategies from human vision and cognition, we build deep learning models of object, place, and events recognition. To this aim, we are building a core of visual knowledge (e.g., Places, a large-scale dataset with 10 million annotated images; Moments in Time, a large-scale dataset of 1 million annotated short videos) that can be used to train artificial systems for visual and auditory event understanding and common-sense tasks, such as identifying where the agent is (i.e., the place), what objects are within reach, what potential surprising events may occur, which types of actions people are performing, and what may happen next.