Automation, Control and Artificial Intelligence
MIT's world class automation, control and AI programs have a keen interest in transportation applications. Ongoing projects include developing control strategies for autonomous vehicles in mixed traffic and using machine learning to predict travel choices. MIT offers courses in machine learning for transportation applications and alumni have founded several successful autonomous vehicle start-ups.
The research labs and faculty working in this area are shown below. You can see a full listing of the people and labs involved with the MIT Mobility Initiative by navigating to the people page and the labs page.
Robert N. Noyce Career Development Associate Professor
Control of Infrastructure Networks, Security of Cyber-Physical Systems, Applied Game Theory and Information Economics
Theresa Seley Professor in Management Science at the Sloan School of Management at MIT
Continuous Optimization, Computational Complexity, Convexity, Computational Science, Mathematical Systems
Dugald C. Jackson Professor in EECS, Co-Director of the Operations Research Center
Online Optimization and Learning, Machine Learning, Decision Making Under Uncertainty
Dibner Professor of the History of Engineering and Manufacturing, Professor of Aeronautics and Astronautics
Autonomy in Human Environments; Precision Navigation; Ultra-Wideband for Urban Transit
Professor of Management and Operations Research, Associate Dean of Business Analytics
Optimization, Stochastic Systems, Machine Learning, Robust Optimization, Transportation and Finance
Assistant Division Head, Homeland Protection and Air Traffic Control, MIT Lincoln Laboratory
Decision Making Under Uncertainty, Risk Assessment, Human / Systems Integration
Distinguished Professor and Department Head, EECS; Deputy Dean of Academics, Schwarzman College of Computing
Nonlinear and Convex Optimization: Theory and Algorithms; Game Theory;
Social and Economic Networks
Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics
Decision Making Under Uncertainty, Robust Control, Adaptive Control, Model Predictive Control, Machine Learning, Reinforcement Learning
Professor of Aeronautics and Astronautics
Design, Analysis, and Implementation of Control and Optimization Algorithms for Large-Scale Cyber-Physical Infrastructures
Aerospace Controls Laboratory
The Aerospace Controls Laboratory researches topics related to autonomous systems and control design for aircraft, spacecraft, and ground vehicles. Areas of theoretical research include decision-making under uncertainty, path planning, activity and task assignment, estimation and navigation, robust, adaptive, and model predictive control, and machine learning methods.
City Science Group
Founded in 1985, the MIT Media Lab is one of the world’s leading research and academic organizations. Unconstrained by traditional disciplines, Media Lab designers, engineers, artists, and scientists strive to create technologies and experiences that enable people to understand and transform their lives, communities, and environments. As part of the MIT Media Lab, the City Science research group proposes that new strategies must be found to create the places where people live and work in addition to the mobility systems that connect them, in order to meet the profound challenges of the future.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
CSAIL is committed to pioneering new approaches to computing that will bring about positive changes in the way people around the globe live, play, and work. They focus on developing fundamental new technologies, conducting basic research that furthers the field of computing, and inspiring and educating future generations of scientists and technologists. With more than 60 research groups working on hundreds of diverse projects, researchers focus on discovering novel ways to make systems and machines smarter, easier to use, more secure, and more efficient.
Connection Science Living Labs
With its novel "Living Labs" paradigm for research in the field, MIT Connection Science brings together interdisciplinary experts to develop, deploy, and test - in actual living environments - new technologies and strategies for safe, trusted, data sharing. MIT is well positioned to take a leadership role in demonstrating not only how organizations can leverage data in the future, but how we collect, manage, and use personal information, from setting appropriate privacy policies to demonstrating systems that can implement it in practice.
Data Science Lab
The Data Science Lab develops analytic techniques and tools for improving decision making in environments that involve uncertainty and require statistical learning. They achieve this vision by exploring theoretical foundations of operational problems and applying them in the development of algorithms that integrate machine learning and stochastic or deterministic optimization techniques. Their methods have been implemented by a large number of companies across a variety of industries such as Airlines, Insurance, Manufacturing and Retail.
Engineering Systems Laboratory
A part of the MIT Department of Aeronautics and Astronautics, the Engineering Systems Laboratory (ESL) studies the underlying principles and methods for designing complex socio-technical systems that involve a mix of architecture, technologies, organizations, policy issues and complex networked operations. Their focus is on aerospace and other systems critical to society such as product development, manufacturing and large scale infrastructures.
Future Urban Mobility at SMART
The Future Urban Mobility IRG's grand challenge is to develop innovative mobility solutions that simultaneously tackle two opposing objectives: To improve the safety, comfort and time associated with transportation, getting individuals and good where they need to be, and when they need to be there; and to reverse the alarming, unsustainable energy and environmental trends associated with transportation, and devise transportation systems that materially enhance sustainability and societal well-being.
Intelligent Transportation Systems Lab
The MIT Intelligent Transportation Systems (ITS) Lab was established in 1990 by Professor Moshe Ben-Akiva. Since its inception, the ITS Lab has conducted numerous studies of transportation systems and developed network modeling and simulation tools. The lab's areas of research include discrete choice and demand modeling techniques, activity-based models, freight transport modeling, and data collection methods for behavioral modeling. Today, lab members are located at MIT's Cambridge campus and its first research center outside of Cambridge: the Singapore-MIT Alliance for Research and Technology (SMART) Centre.
International Center for Air Transportation
The mission of the MIT International Center for Air Transportation is to undertake research and educational programs which discover and disseminate the knowledge and tools underlying a global air transportation industry driven by new technologies. Airline management, airport security, air transportation economics, fleet scheduling, traffic flow management and airport facilities development represent areas of great interest to the MIT faculty and are of vital importance to international air transportation. ICAT is a physical and intellectual home for these activities. The ICAT, and its predecessors, the Aeronautical Systems Laboratory (ASL) and Flight Transportation Laboratory (FTL), have pioneered several concepts in air traffic management and flight deck automation and displays that are now in common use.
JTL Urban Mobility Lab
The JTL Urban Mobility Lab at MIT brings behavioral science and transportation technology together to shape travel behavior, design mobility systems, and improve transportation policies. They apply this framework to managing automobile ownership and usage, optimizing public transit planning and operation, promoting active modes of walking and cycling, governing autonomous vehicles and shared mobility services, and designing multimodal urban transportation systems.
Laboratory for Information and Decision Systems (LIDS)
The Laboratory for Information and Decision Systems (LIDS) at MIT is an interdepartmental research center committed to advancing research and education in the analytical information and decision sciences, specifically in systems and control, communications and networks, and inference and statistical data processing. Throughout its history, LIDS has been at the forefront of major methodological developments in a wide range of fields, including: telecommunications, information technology, the automotive industry, energy, defense, and human health. Building on past innovation and bolstered by a collaborative atmosphere, LIDS members continue to make breakthroughs that cut across traditional boundaries.
Quest for Intelligence
MIT Quest addresses two fundamental questions: How does human intelligence work, in engineering terms? And how can we use our understanding of human intelligence to build smarter machines for the benefit of society? As part of our mission, we are developing customized AI tools for non-AI researchers, which could accelerate progress in many fields. We see an opportunity to achieve a deeper understanding of intelligence through the kind of basic research that leads to unexpected breakthroughs. We aspire for our new knowledge and newly built tools to serve the public good, in our nation and around the world.
Resilient Infrastructure Systems Lab
The Resilient Infrastructure Systems Lab seeks to improve the robustness and security of critical infrastructure systems by developing tools to detect and respond to incidents, both random and adversarial and by designing incentive mechanisms for efficient infrastructure management. They are working on the problems of cyber-physical security, failure diagnostics and incident response, network monitoring and control, and demand management in real-world infrastructures. They mainly focus on cyber-physical infrastructure systems for electric power, transportation, and urban water and natural gas networks.
Robust Robotics Group
The research goals of the Robust Robotics Group are to build unmanned vehicles that can fly without GPS through unmapped indoor environments, robots that can drive through unmapped cities, and to build social robots that can quickly learn what people want without being annoying or intrusive. Such robots must be able to perform effectively with uncertain and limited knowledge of the world, be easily deployed in new environments and immediately start autonomous operations with no prior information. They specifically focus on problems of planning and control in domains with uncertain models, using optimization, statistical estimation and machine learning to learn good plans and policies from experience.
Senseable City Lab
The real-time city is real! As layers of networks and digital information blanket urban space, new approaches to the study of the built environment are emerging. The way we describe and understand cities is being radically transformed, as are the tools we use to design them. The mission of the Senseable City Laboratory, a research initiative at MIT, is to anticipate these changes and study them from a critical point of view.
Frontier of Transportation Research
Survey of the latest transportation research offered by 12 MIT faculty each presenting their ongoing research. Students are required to attend the classes, read the assigned articles, and write a brief reflection memo.
Deep Learning for Urban Mobility
Explores deep learning (DL) methods for urban mobility applications. Covers concepts of algorithmic prediction, interpretability, causality, and fairness in the context of urban mobility system design and policy making. Topics include demand prediction at both individual and aggregate levels, decision making with and without uncertainty, vehicle and ride sharing, built environment and travel behavior, traffic prediction and control, maps and information provision, and multimodal system design. Students learn intuitions and methods in DNN, CNN, RNN and reinforcement learning, build hands-on models using real-world datasets, and design and implement group projects. At the intersection of machine learning methods and urban mobility applications, the course seeks to reconcile the tension between generic-purpose models and domain-specific knowledge. Furthermore, the course envisions and critically reflects on how machine learning methods shape transportation research and mobility industry, and examines the potentials and pitfalls of their applications in urban mobility business and policies.
Visual Navigation for Autonomous Vehicles
Covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles (e.g., mobile robots, self-driving cars, drones). Topics include geometric control, 3D vision, visual-inertial navigation, place recognition, and simultaneous localization and mapping. Provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and knowledge of the fundamentals of 2-view and multi-view geometric vision for real-time motion estimation, calibration, localization, and mapping. The theoretical foundations are complemented with hands-on labs based on state-of-the-art mini race car and drone platforms. Culminates in a critical review of recent advances in the field and a team project aimed at advancing the state-of-the-art.
Introduction to Airline Transport Aircraft Systems and Automation
Intensive one-week subject that uses the Boeing 767 aircraft as an example of a system of systems. Focuses on design drivers and compromises, system interactions, and human-machine interface. Morning lectures, followed by afternoon desktop simulator sessions. Critique and comparison with other transport aircraft designs. Includes one evening at Boston Logan International Airport aboard an aircraft.
Advanced Autonomous Robotic Systems
Students design an autonomous vehicle system to satisfy stated performance goals. Emphasizes both hardware and software components of the design and implementation. Entails application of fundamental principles and design engineering in both individual and group efforts. Practice in written and oral communication provided. Students showcase the final design to the public at the end of the term.
Theory and application of probabilistic techniques for autonomous mobile robotics. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamic models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; application to autonomous marine (underwater and floating), ground, and air vehicles.
Robotics: Science and Systems
Presents concepts, principles, and algorithmic foundations for robots and autonomous vehicles operating in the physical world. Topics include sensing, kinematics and dynamics, state estimation, computer vision, perception, learning, control, motion planning, and embedded system development. Students design and implement advanced algorithms on complex robotic platforms capable of agile autonomous navigation and real-time interaction with the physical word. Students engage in extensive written and oral communication exercises.
Reinforcement Learning: Foundations and Methods
This subject counts as a Control concentration subject. Reinforcement learning (RL) as a methodology for approximately solving sequential decision-making under uncertainty, with foundations in optimal control and machine learning. Finite horizon and infinite horizon dynamic programming, focusing on discounted Markov decision processes. Value and policy iteration. Monte Carlo, temporal differences, Q-learning, and stochastic approximation. Approximate dynamic programming, including value-based methods and policy space methods. Special topics at the boundary of theory and practice in RL. Applications and examples drawn from diverse domains. While an analysis prerequisite is not required, mathematical maturity is necessary. Enrollment limited
Discrete Probability and Stochastic Processes
Provides an introduction to tools used for probabilistic reasoning in the context of discrete systems and processes. Tools such as the probabilistic method, first and second moment method, martingales, concentration and correlation inequalities, theory of random graphs, weak convergence, random walks and Brownian motion, branching processes, Markov chains, Markov random fields, correlation decay method, isoperimetry, coupling, influences and other basic tools of modern research in probability will be presented. Algorithmic aspects and connections to statistics and machine learning will be emphasized.
Topics on the engineering and analysis of network protocols and architecture, including architectural principles for designing heterogeneous networks; transport protocols; Internet routing; router design; congestion control and network resource management; wireless networks; network security; naming; overlay and peer-to-peer networks. Readings from original research papers.
Applied Machine Learning
Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; classification, regression, reinforcement learning; and methods such as linear classifiers, feed-forward, convolutional, and recurrent networks. Students taking graduate version complete different assignments. Meets with 6.036 when offered concurrently. Recommended prerequisites: 18.06 and 6.006. Enrollment limited; no listeners.
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning.
Statistics, Computation and Applications
Hands-on analysis of data demonstrates the interplay between statistics and computation. Includes four modules, each centered on a specific data set, and introduced by a domain expert. Provides instruction in specific, relevant analysis methods and corresponding algorithmic aspects. Potential modules may include medical data, gene regulation, social networks, finance data (time series), traffic, transportation, weather forecasting, policy, or industrial web applications. Projects address a large-scale data analysis question. Students taking graduate version complete additional assignments. Limited enrollment; priority to Statistics and Data Science minors and to juniors and seniors.
Data Mining: Finding the Models and Predictions that Create Value
Introduction to data mining, data science, and machine learning, methods that assist in recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, pointof-sale devices, bar-code readers, medical databases, and other sources. Topics include logistic regression, association rules, treestructured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software focusing on R. Term project required. Meets with 15.062 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
Data Science and Machine Learning for Supply Chain Management
Introduces data science and machine learning topics in both theory and application. Data science topics include database and API connections, data preparation and manipulation, and data structures. Machine learning topics include model fitting, tuning and prediction, end-to-end problem solving, feature engineering and feature selection, overfitting, generalization, classification, regression, neural networks, dimensionality reduction and clustering. Covers software packages for statistical analysis, data visualization and machine learning. Introduces best practices related to source control, system architecture, cloud computing frameworks and modules, security, emerging financial technologies and software process. Applies teaching examples to logistics, transportation, and supply chain problems.