Artificial Intelligence and Decision-making combines intellectual traditions from across computer science and electrical engineering to develop techniques for the analysis and synthesis of systems that interact with an external world via perception, communication, and action; while also learning, making decisions and adapting to a changing environment.
Our research explores the foundations of machine learning and decision systems (artificial intelligence, reinforcement learning, statistics, causal inference, systems and control); the building blocks of embodied intelligence (computer vision, NLP, robotics); applications to real-world autonomous systems; life sciences; and the interface between data-driven decision-making and society.
Our educational mission is to provide students with the strong mathematical and algorithmic foundations needed to build robust systems that can extrapolate from data to insights and decisions. Our students will learn the skills needed to understand data, model real-world phenomena, and build the future in which technology truly benefits humanity.
The future of our society is interwoven with the future of data-driven thinking—most prominently, artificial intelligence is set to reshape every aspect of our lives. Research in this area studies the interface between AI-driven systems and human actors, exploring both the impact of data-driven decision-making on human behavior and experience, and how AI technologies can be used to improve access to opportunities. This research combines a variety of areas including AI, machine learning, economics, social psychology, and law.
Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.
Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.
We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.
The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.
Our research encompasses all aspects of speech and language processing—ranging from the design of fundamental machine learning methods to the design of advanced applications that can extract information from documents, translate between languages, and execute instructions in real-world environments.
Research in this area focuses on developing efficient and scalable algorithms for solving large scale optimization problems in engineering, data science and machine learning. Our work also studies optimal decision making in networked settings, including communication networks, energy systems and social networks. The multi-agent nature of many of these systems also has led to several research activities that rely on game-theoretic approaches.
Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.
Signal processing focuses on algorithms and hardware for analyzing, modifying and synthesizing signals and data, across a wide variety of application domains. As a technology it plays a key role in virtually every aspect of modern life including for example entertainment, communications, travel, health, defense and finance.
Our theoretical research includes quantification of fundamental capabilities and limitations of feedback systems, inference and control over networks, and development of practical methods and algorithms for decision making under uncertainty.
￼Massachusetts Microelectronics Internship Program: a big focus on critical tiny components
EECS Alliance–backed program seeks to get more Massachusetts students into the microelectronics game
Aleksander Madry, Asu Ozdaglar, and Luis Videgaray, co-chairs of the AI Policy Forum, discuss key issues facing the AI policy landscape today.
The AI Policy Forum (AIPF) is an initiative of the MIT Schwarzman College of Computing to move the global conversation about the impact of artificial intelligence from principles to practical…
By continuously monitoring a patient’s gait speed, the system can assess the condition’s severity between visits to the doctor’s office.
“Kids are people too!” Throughout his career, Professor Hal Abelson has worked to make information technology more accessible to people of all ages.
Professor Hal Abelson has dedicated his career to making information technology more accessible to all and empowering people — kids, in particular — through computer science. But his…
Researchers increase the accuracy and efficiency of a machine-learning method that safeguards user data.