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Computational Design for Manufacturing

July 16-19, 2018

Over the next few decades, we are going to transition to a new economy where highly complex, customizable products are manufactured on demand by flexible robotic systems. This change is already underway in a number of fields. 3D printers are revolutionizing production of metal parts in aerospace, automotive, and medical industries. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity. This course gives an introduction to the new field of computational design that is essential for the next revolution in manufacturing. Participants will be given an overview of the advanced manufacturing hardware, methods for creating digital materials, and computational design of objects at voxel-level. The course will cover generative design workflows that automatically translate functional specifications of objects to manufacturable designs. The course will also introduce participants to AI methods for computational design that utilize expert knowledge and large data repositories. Finally, the course will showcase new workflows for design across multiple domains (e.g., shape, materials, control, and software) that simplify the process and fully utilize the design space.

Engineering Leadership for Emerging Leaders

July 16-20, 2018

Offered by the premier Gordon-MIT Engineering Leadership Program, this five-day course is designed to equip you with the skills and perspectives needed to lead yourself and others in today’s engineering and technology environments. You will improve your leadership skills by learning from the latest breakthroughs in the practice of leadership within a program that draws on a variety of teaching methods, especially hands-on learning. Like the practice of leadership itself, this program will be high-contact, high-energy, and consequential.

The transition to becoming an engineering leader is one of the most promising, yet challenging experiences that engineering professionals can face. The promise comes from becoming a new kind of professional; one who can mobilize sometimes-conflicting individuals around a shared vision, solve problems through “real” teamwork, and motivate people to deliver their best results. The challenge comes from learning to work in an entirely new way; from relying solely on oneself to deliver individual results to leading others to deliver collective results. Herein lies the nature of the delicate relationship between leadership and followership.
During our five-day program, you will:

  • Enhance your understanding of the nature of leadership and followership
  • Build a foundation of team-building skills
  • Develop and deliver an inspiring and shared vision
  • Discover new ways to lead and motivate others in technical environments
  • Gain support for your ideas in environments characterized by conflicting stakeholder needs
  • Learn to manage conflicts through negotiations and constructive dialogues

Innovation: Beyond the Buzzword

July 16-18, 2018

We live in an age of exponential change in which rapid innovation is disrupting and unseating incumbent products and industries, creating new technological frontiers, and challenging nearly everything we think we know about business. For instance, think Uber and the end of the medallion taxi industry. Think Airbnb in twice as many countries as Hilton in less than 5 percent of the time. Think Tesla. Think Oculus. But beyond using the "buzzword," can you really define innovation?

In this course, which is centered on the concept of Design Thinking, your answer to that question will come from actually involving yourself in the activity of innovating.

The course will include lectures from faculty and guests, discussions of case studies in innovation models and methods, and learning expeditions on and beyond the MIT campus. But it will also go beyond these traditional classroom activities to include hands-on experiences with some cutting-edge innovations as well as group work and a class hackathon to engage in genuine innovating ? and through that, to gain an understanding beyond the buzzword. Participants will emerge as more critical thinkers, knowledgeable about what innovation is (and is not), how it happens, how to discern meaningful trends in design and technology, and how to identify opportunities and propose innovative products, services, and experiences. Active class participation, a willingness to engage with others in a creative process, and a recognition that you might have a lot to learn about innovation are all prerequisites for the course.

Who Should Attend: To facilitate the cross-pollination of ideas, approaches, and critical thought, professionals from all industries are welcome. People from across the functional business spectrum will find the course valuable, including strategy leaders, directors of innovation and technology, product managers, engineers, marketers, and R&D personnel. All participants must come with a willingness and enthusiasm to engage and be ready to share their particular passions and expertise.

Principles and Models for System Architecture

July 16-20, 2018

The complexity of products is increasing as we demand additional functionality and higher performance from them. In many cases, we must move to new architectures in order to accommodate this complexity. Furthermore, novel products and systems development require the involvement of and communication between professionals with multiple disciplinary backgrounds as well as with external stakeholders. This promise of this collaboration is to detecting failure modes and constraints early on during its lifecycle, but in practice the early phase of product development is often unstructured. In this course, we will show that complex engineering systems have a set of common principles that cuts across the traditional fields of engineering. The discipline of System Architecture (SA) has been growing in response to this increase in system and product complexity. System architecture is an early lifecycle activity that determines the systems concept and key technical tradeoffs. Nurturing systems thinking and engineering skills, the course begins with System Architecture as a series of decisions that frame the form to function mapping. Learners are exposed to a number of architecture representations, including the Object-Process Methodology (OPM) and the Design Structure Matrix (DSM). Learners gain hands-on modeling experience on a system of their choice by building a series of model deliverables through the course. Learners are exposed to a selection of advanced architecting topics, notably creating tradespaces of designs and the management role of the architect.

Humans, Technology, and the Future of Work

July 16-18, 2018

This course examines the impact that increasing technology use has on workforce productivity. Attendees can expect to, 1) grow their understanding of existing workforce performance limitations, 2) determine how technology can help circumvent these limitations, 3) grasp the productivity challenges technology poses, and 4) explore possible solutions for navigating these challenges in the complex Future of Work. Case studies examined will cut across industries and challenge participants to conceptualize how technological use may affect and be affected by the Future of Work. The course material is interdisciplinary, drawing on literature from psychology, economics, demography, law and ethics.

Machine Learning for Healthcare

July 16-17, 2018

With massive amounts of data flowing from EMRs, wearables and countless other new sources, the potential for machine learning and AI to transform healthcare is perhaps more drastic and profound than any other industry. But there are unique challenges that exist in healthcare that make it difficult to apply new technologies in the industry, including patient privacy issues, the lack of interoperability, and the diversity of digital health data. In this course, you'll gain practical knowledge that will enable you to overcome these hurdles and apply the latest advances in healthcare AI tools and techniques to:

  • Automate medical discoveries
  • Predict patient outcomes
  • Model disease progression
  • Identify & manage high-risk patients
  • Implement patient health initiatives
  • Prevent high-cost care
    • You'll also come away with a strong understanding of important ethical and moral issues associated with the use of machine learning in healthcare, and learn how to develop fair and accountable algorithms that ensure unbiased care.

Modeling and Optimization for Machine Learning

July 16-20, 2018

Numerical modeling is the skill of reducing a messy engineering or computational problem to a mathematical form that can be solved by using standard algorithms and techniques. By recognizing mathematical patterns ?in the wild,? participants will develop an intuition for which problems are solvable using standard numerical modeling techniques and gain the knowledge and skills to then solve them. Computer science is experiencing a fundamental shift in its approach to modeling and problem solving. Early computer scientists primarily studied discrete mathematics, focusing on structures like databases and arrays composed of distinct pieces. With the introduction of modern applications in ?big data,? three-dimensional scanning, machine learning, and noisy sensor communications, practitioners now must design robust methods for processing real-valued data. The latest generation of programmers, computer scientists, and engineers must be able to reason about not just bits-and-bytes, but also calculus, linear algebra, and optimization. By the end of the course, participants will learn how to boil real-world challenges down to their computational essence to make a reasonable estimate of how difficult it would be to design a numerical method to solve them. We will cover a breadth of possible tools, from numerical linear algebra to convex programming and stochastic/deterministic gradient descent, in the context of practical problems drawn from emerging applications in vision, learning, and graphics. Coding and mathematical exercises will reinforce these ideas and expose participants to standard software packages for optimization.

Additive Manufacturing: From 3D Printing to the Factory Floor

July 23, 2018 to July 27, 2018

This course will build a comprehensive understanding of additive manufacturing (AM) processes and their implications for product development and manufacturing operations. Lectures will analyze AM fundamentals, materials, and process capabilities. This content will then be related to applications spanning industries including aerospace, medical devices, electronics, architecture, and consumer products. Lab sessions will provide hands-on experience with desktop 3D printers. Participants will design, fabricate, and measure components, and will identify future opportunities via case studies.

Additive manufacturing (AM) processes were first demonstrated more than twenty five years ago; however, only recently has broad industrial and consumer interest ignited, with potential implications ranging from ubiquitous personal fabrication to disruption of traditional supply chains. The goal of this course is to present a comprehensive overview of AM, spanning from fundamentals to applications and technology trends. Participants will learn the fundamentals of AM of polymers, metals, composites, and biomaterials, and will realize how process capabilities (rate, cost, quality) are determined by the material characteristics, process parameters, and machine designs. Application areas including aerospace components, electronics, medical devices, and consumer products will be discussed via detailed examples and case studies. Particular emphasis will be placed on emerging metal- and powder-based AM technologies, and related design principles and process standards. Lab sessions will provide hands-on experience with a variety of state-of-the-art desktop 3D printers and scanners. Participants will design, fabricate, and measure test parts, and will perform experiments to explore process limits. The course will conclude with a perspective on needs for future advancement of AM and major opportunities spanning many related business and technical domains.