Networks and Systems
MIT faculty lead the way in network and graph theory, often applying newly developed methods to transportation problems. This research guides mobility infrastructure investment, evacuation planning and cybersecurity risk analysis. Many modern transportation systems rely on mobile networks which are subject to disruption; autonomous vehicle systems may be even more vulnerable. Ongoing research at MIT seeks to understand these risks in order to design efficient, robust systems and networks.
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
Dugald C. Jackson Professor in EECS, Co-Director of the Operations Research Center
Online Optimization and Learning, Machine Learning, Decision Making Under Uncertainty
Professor of Aeronautics and Astronautics and Engineering Systems
Aerospace Systems, Engineering Systems, Technology Development, Multidisciplinary Design Optimization
Assistant Group Leader, Air Traffic Control Systems, MIT Lincoln Laboratory
Aviation Cybersecurity, Weather Sensing, System/Software Architectures, System Engineering
Professor of Aeronautics and Astronautics
Design, Analysis, and Implementation of Control and Optimization Algorithms for Large-Scale Cyber-Physical Infrastructures
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
Charles and Ann Spaulding Career Development Associate Professor of Urban Science and Planning
Spatial Analysis, Walkability, Public Transport, Business Location Patterns, Urban Design
Director of the MIT Megacity Logistics Lab; Director of the MIT CAVE Lab
Multi-tier Distribution Network Design, Urban Logistics, Last-Mile Delivery, Urban Freight Policy, Data Analytics and Visualization
Center for Energy and Environmental Policy Research
Since 1977, the Center for Energy and Environmental Policy Research (CEEPR) has been a focal point for research on energy and environmental policy at MIT. CEEPR promotes rigorous, objective research for improved decision making in government and the private sector, and secures the relevance of its work through close cooperation with industry partners from around the globe. Drawing on the unparalleled resources available at MIT, affiliated faculty and research staff as well as international research associates contribute to the empirical study of a wide range of policy issues related to energy supply, energy demand, and the environment.
Center for Ocean Engineering
Today, MIT is at the forefront of ocean science and engineering, with significant efforts in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. In addition, the Naval Construction program provides advanced graduate education on the design of naval ships and vehicles. The Center is a focal point for interdepartmental collaborations, interactions with other MIT schools, as well as outside the Institute.
Center for Transportation and Logistics
For more than four decades, the MIT Center for Transportation & Logistics (MIT CTL) has been a world leader in supply chain management education and research. MIT CTL has made significant contributions to supply chain and logistics and has helped numerous companies gain competitive advantage from its cutting-edge research. Launched in 1973, the MIT Center for Transportation & Logistics (CTL) is a dynamic solutions-oriented environment where students, faculty, and industry leaders pool their knowledge and experience to advance supply chain education and research.
City Form Lab
The City Form Lab at MIT focuses on urban design, planning and real-estate research. We develop new software tools for researching city form; use cutting-edge spatial analysis and statistics to investigate how urban form and land-use developments affect urban mobility and business location choices; and develop creative design and policy solutions for contemporary urban challenges. By bringing together multi-disciplinary urban research expertise and excellence in design, we develop context sensitive and timely insight about the role of urban form in affecting the quality of life in 21st century cities. CFL involves inter-disciplinary researchers and students interested in urban design, planning, transportation, spatial analysis and decision-making.
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.
The MIT Energy Initiative is MIT's hub for energy research, education, and outreach, connecting faculty, students, and staff to develop the knowledge, technologies, and solutions that will deliver clean, affordable, and plentiful sources of energy. Their mission is to develop low- and no-carbon solutions that will efficiently, affordably, and sustainably meet global energy needs while minimizing environmental impacts, dramatically reducing greenhouse gas emissions, and mitigating climate change.
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.
Institute for Data, Systems and Society (IDSS)
The mission of IDSS is to advance education and research in state-of-the-art analytical methods in information and decision systems, statistics and data science, and the social sciences, and to apply these methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.
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.
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.
MIT Digital Supply Chain Transformation
Digital transformation is now a keystone of operational, organizational, and technological structures for companies and supply chains who desire to be competitive in the vision of the future business environment. Our work aims to support organizationally adaptable, technologically compatible, and economically viable transformation for improving performance.
The primary research examines new collaborative paradigms that arise while implementing different new digital technologies in supply chains. Our research domains are digital platforms, multidimensional collaboration, digital capabilities and Artificial Intelligence (AI) in supply chains. Our research fosters more visible, efficient, flexible and resilient networks. We apply quantitative research methodologies in order to assess how data-driven ecosystems create value.
Megacity Logistics Lab
The Megacity Logistics Lab brings together business, logistics, and urban planning perspectives to develop appropriate technologies, infrastructures, and policies for sustainable urban logistics operations. Their work aims to promote new urban delivery models, from unattended home delivery solutions to smart locker systems, to click & collect services, to drone delivery. They are pushing the limits of existing logistics network designs as future city logistics networks need to support omni-channel retail models, smaller store formats, increased intensity of deliveries, coordinate multiple transshipment points, engage a wider range of vehicle technologies - including electric and autonomous vehicles - and support complex inventory balancing and deployment strategies.
Operations Research Center
The mission of the Operations Research Center is to impact the world by educating students in the fields of Operations Research and Analytics who will become leaders in either academia or industry, generate new knowledge via research that will be used in educating future generations of students around the world and impact society via research by solving some of the world's most significant problems.
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.
Sociotechnical Systems Research Center
The MIT Sociotechnical Systems Research Center (SSRC) is an interdisciplinary research center that focuses on the study of high-impact, complex, sociotechnical systems that shape our world. SSRC brings together faculty, researchers, students and staff from across MIT to study and seek solutions to complex societal challenges that span healthcare, energy, infrastructure networks, environment and international development. Their mission is to develop collaborative, holistic and systems-based approaches that combine knowledge and expertise from engineering and social sciences.
The MIT Transit Lab leverages the value of large-scale, long-term research collaborations across transit agencies. Starting in 1992 under the leadership of Professor Nigel Wilson, the Lab has collaborated with metropolitan transit agencies and departments of transportation worldwide, developing and implementing technology for transit operations and planning. Past and ongoing research sponsors include the Chicago Transit Authority (CTA), Massachusetts Bay Transportation Authority (MBTA), Transport for London (TfL), and Mass Transit Railway (MTR, Hong Kong). These long-term engagements, in addition to projects with other transit agencies and international research centers, provide graduate students unique opportunities for applied research.
Introduction to Network Models
Provides an introduction to complex networks, their structure, and function, with examples from engineering, applied mathematics and social sciences. Topics include spectral graph theory, notions of centrality, random graph models, contagion phenomena, cascades and diffusion, and opinion dynamics.
Transportation Systems Analysis: Performance & Optimization
Problem-motivated introduction to methods, models and tools for the analysis and design of transportation networks including their planning, operations and control. Capacity of critical elements of transportation networks. Traffic flows and deterministic and probabilistic delay models. Formulation of optimization models for planning and scheduling of freight, transit and airline systems, and their solution using software packages. User- and system-optimal traffic assignment. Control of traffic flows on highways, urban grids, and airspace.
Resilient Infrastructure Networks
Control algorithms and game-theoretic tools to enable resilient operation of large-scale infrastructure networks. Dynamical network flow models, stability analysis, robust predictive control, fault and attack diagnostic tools. Strategic network design, routing games, congestion pricing, demand response, and incentive regulation. Design of operations management strategies for different reliability and security scenarios. Applications to transportation, logistics, electric-power, and water distribution networks.
Planning and Design of Airport Systems
Focuses on current practice, developing trends, and advanced concepts in airport design and planning. Considers economic, environmental, and other trade-offs related to airport location, as well as the impacts of emphasizing "green" measures. Includes an analysis of the effect of airline operations on airports. Topics include demand prediction, determination of airfield capacity, and estimation of levels of congestion; terminal design; the role of airports in the aviation and transportation system; access problems; optimal configuration of air transport networks and implications for airport development; and economics, financing, and institutional aspects. Special attention to international practice and developments.
Public Transportation Analytics and Planning
Students will gain experience processing, visualizing, and analyzing urban mobility data, with special emphasis on models and performance metrics tailored to scheduled, fixed-route transit services. The evolution of urban public transportation modes and services, as well as interaction with emerging on-demand services, will be covered. Instructors and guest lecturers from industry will discuss both methods for data collection and analysis, as well as organizational, policy, and governance constraints on transit planning. In assignments, students will practice using spatial database, data visualization, network analysis, and other software to shape recommendations for transit that effectively meets the future needs of cities.
Robust Modeling, Optimization, and Computation
Introduces modern robust optimization, including theory, applications, and computation. Presents formulations and their connection to probability, information and risk theory for conic optimization (linear, second-order, and semidefinite cones) and integer optimization. Application domains include analysis and optimization of stochastic networks, optimal mechanism design, network information theory, transportation, pattern classification, structural and engineering design, and financial engineering. Students formulate and solve a problem aligned with their interests in a final project.
Air Traffic Control
Introduces the various aspects of present and future Air Traffic Control systems. Descriptions of the present system: systems-analysis approach to problems of capacity and safety; surveillance, including NAS and ARTS; navigation subsystem technology; aircraft guidance and control; communications; collision avoidance systems; sequencing and spacing in terminal areas; future directions and development; critical discussion of past proposals and of probable future problem areas.
Network Science and Models
Introduces the main mathematical models used to describe large networks and dynamical processes that evolve on networks. Static models of random graphs, preferential attachment, and other graph evolution models. Epidemic propagation, opinion dynamics, social learning, and inference in networks. Applications drawn from social, economic, natural, and infrastructure networks, as well as networked decision systems such as sensor networks.
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.