Prof. Larson is Mitsui Professor in the MIT Institute for Data, Systems, and Society. He is author, co-author, or editor of six books and author of over 85 articles, primarily in the fields of urban service systems (especially emergency response systems), queueing, logistics, disaster management, disease dynamics, dynamic pricing of critical infrastructures, education, and workforce planning.

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The implications of additive manufacturing (AM) span the complete product life-cycle, from concept-stage design to service part fulfillment. Recent advances, including industrially viable high-speed AM processes, improved materials, and optimization software, now enable AM to be considered hand-in-hand with conventional production technologies. In short, AM is the cornerstone of future digital production infrastructure. Moreover, the unprecedented design flexibility of AM allows us to invent products with new levels of performance, and to envision supply chains that achieve rapid, responsive production with reduced cost and risk.
John Hart pic
John Hart

Participating Instructor

John Hart is Professor of Mechanical Engineering and Director of the Laboratory for Manufacturing and Productivity and Center for Additive and Digital Advanced Production Technologies (APT) at MIT. John’s research focuses on additive manufacturing, nanostructured materials, and the integration of computation and automation in process discovery.

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In this next-level follow-up to our Crisis Management & Business Resiliency Program, we deep dive into issues you and your industry peers chose. Topics are unique to each year’s course, as selected by previous years’ attendees of the CM&BR Program. Participants will acquire knowledge and insights from leading industry topical experts as well as your course colleagues. We can also discuss your own pressing organizational, program, management and resiliency challenges privately or in class. Student Projects: Individual teams of students will select industry issues or specific problems/processes for study. These Projects require students to work together in small groups during the week and present their results to the Class at the end of the course. Following the Course, if interested, a Team’s Student Project can result in a published article and/or conference presentation.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution? Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.

Vivienne Sze is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and leads the Research Lab of Electronics’ Energy-Efficient Multimedia Systems research group. Her group works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things.

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