Prof. Aude Oliva

Executive Director, Quest for Intelligence
Executive Director, MIT-IBM Watson AI Lab

Primary DLC

Computer Science and Artificial Intelligence Laboratory

MIT Room: 32-D432

Areas of Interest and Expertise

Automatic Visual Understanding
Computational Perception and Cognition
Human Visual Intelligence
Big Data

Research Summary

Aude Oliva's 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.

Recent Work

  • Video

    2.25.21 AI Autonomy Roundtable

    February 25, 2021Conference Video Duration: 112:29

    Sophie Vandebroek
    Board Director, Trustee, Scholar
    2020 MIT School of Engineering Inaugural Visiting Scholar
    Former COO of IBM Research
    Aude Oliva
    MIT Director, the MIT-IBM Watson AI Lab
    Director, MIT Quest Corporate
    Bilge Yildiz
    Professor, Nuclear Science and Engineering
    Professor, Materials Science and Engineering
    Tod Newman
    Lead, Center for Artificial Intelligence & Machine Learning, Raytheon Technologies
    Lauren Barozie
    FCAS, Senior Director, Analytics Transformation, GRS AA Hub at Liberty Mutual Insurance
    Malcolm McRae
    Head of AI and Advanced Analytics, Vale
    Lama Nachman
    Intel Fellow, Director of Human & AI Systems Research Lab, Intel

    David Mindell
    Dibner Professor, History of Engineering and Manufacturing
    Professor, Aeronautics and Astronautics and Engineering Systems
    Founder & CEO, Humatics
    Nicholas Roy
    Bisplinghoff Professor, Aeronautics & Astronautics
    Director of Quest Systems Engineering, MIT Quest for Intelligence
    John Tylko
    Chief Innovation Officer, Aurora Flight Sciences, A Boeing Company
    Dimitris Bountolos
    Chief Information & Innovation Officer, Ferrovial
    Sherif Marakby
    Executive Vice President of Research and Development

    Tomás Palacios
    Professor, Electrical Engineering and Computer Science

    Aude Oliva - 2019 Vienna Conference

    April 3, 2019Conference Video Duration: 47:54

    MIT Quest for Intelligence

    Imagine if the next breakthrough in artificial intelligence came from the root of intelligence itself: the human brain. At a time of rapid advances in intelligence research across many disciplines, the Intelligence Quest will encourage researchers to investigate the societal implications of their work as they pursue hard problems lying beyond the current horizon of what is known. Some of these advances may be foundational in nature, involving new insight into human intelligence, and new methods to allow machines to learn effectively. Others may be practical tools for use in a wide array of research endeavors, such as disease diagnosis, drug discovery, materials and manufacturing design, automated systems, synthetic biology, and finance. Today we set out to answer two big questions: How does human intelligence work in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines, to the benefit of society?
     
    2019 MIT Europe Conference in Vienna