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Advances in Food Innovation

June 18, 2018 to June 22, 2018

The past decades have resulted in unparalleled progress in food technology, driven by innovation that spans across disciplines as diverse as agriculture, big data and machine learning, and materials science. This intense course will cover different aspects of innovative paradigms to optimize and adapt existing processes as they pertain to the production, distribution, and consumption of food. Participants will explore groundbreaking insights at the interfaces of traditional disciplinary boundaries and receive practical training in creative methods, innovation, and entrepreneurship through a variety of interactive learning experiences. Integrated around key concepts in food, participants will be exposed to multiple perspectives in engineering, technology, and science. The course encompasses both scientific and entrepreneurial aspects, including startups in the food industry and creativity by world-leading chefs. This course focuses on four fundamental areas that underpin food innovation: The application of advanced technologies, such as new materials, data, and machines, in both conventional and unconventional agricultural production The use of data and modeling to improve the production and distribution of food by enhanced precision by using nanotechnology, biotechnology, and other cutting edge engineering solutions, combined with large-scale data analytics and simulation Food access and distribution, including new technologies for preservation and presentation and the use of unconventional ingredients New and creative methods at the interface of science, engineering, and the arts that will push the boundaries of conventional methods to generate new tasting experiences. WHO SHOULD ATTEND
This course is highly interactive and immerses participants into key frontier technologies with hands-on participation. It is designed for people working in food-related industry roles, such as VPs, directors, or managers of R&D; research scientists and engineers; chefs and restaurant owners; and government administrators in food areas (U.S. or overseas). Industries that would benefit from this course include chemical, machinery, environmental, commodity production (agricultural), seed manufacturing, biotechnology, pharmaceutical, venture capital, and agricultural non-profits including cooperatives. The course will be particularly suitable for members of the food innovation space including food startups, restaurants, and innovative distribution solutions.

Design of Electric Motors, Generators and Drive Systems

June 18-22, 2018

This course focuses on the analysis and design of electric motors, generators, and drive systems, placing special emphasis on the design of machines for electric drives, including traction drives and drive motors for robots. Participants will gain extensive hands-on exposure through computer-based laboratory exercises using MATLAB and a hardware build session in our instructional laboratories.

Discrete Choice Analysis: Predicting Demand and Market Shares

June 18-22, 2018

This one-week program undertakes an in-depth study of discrete choice models (logit, nested logit, generalized extreme value, probit, logit mixtures), data collection, specification, estimation, statistical testing, forecasting, and application. The covered topics include analysis of revealed and stated preferences data, sampling, and simulation-based estimation, discrete panel data, Bayesian estimation, discrete-continuous models, menu choice, and models with latent variables. The course includes practical application sessions where participants will be provided with discrete choice software to learn how to estimate and test discrete choice models taught in lecture using real databases, and gain hands on experience in using new discrete choice techniques for practical applications. By examining actual case studies of discrete choice methods, students will be familiarized with problems of model formulation, testing, and forecasting.

Discrete choice models are widely used for the analysis of individual choice behavior and can be applied to choice problems in many fields such as economics, environmental management, urban planning, etc. For example, discrete choice modeling is used in marketing research to guide product positioning, pricing, product concept testing, and many other areas of strategic and tactical interest. Recent applications to predict changes in demand and market shares include areas such as choice of travel mode, coffee brand, telephone service, soft drinks and other foods, and choice of durables such as automobiles, air conditioners, and houses.

Who Should Attend

This program is intended for academics and professionals interested in learning new discrete choice techniques and how to predict choice and forecast demand. They will gain hands-on experience in applying discrete choice software in real-world case studies. Participants need only have a basic working knowledge of statistical methods.

High-Speed Imaging for Motion Analysis: Systems and Techniques

June 18-21, 2018

This program is designed for scientists, engineers, and photographers who need to gather data on rapidly moving subjects and events for study, motion analysis, and trouble-shooting. Mornings are spent in the lecture hall learning the fundamentals for lighting, imaging technologies, and motion analysis. Afternoons are spent making high-speed images in the laboratory. In addition to carrying out the standard techniques, attendees will try out the latest in high-speed-imaging equipment, with the manufacturer's representatives there to provide hands-on education and experience with the systems. The course is held at the Edgerton Center at MIT - the home of Doc Edgerton's Stroboscopic Light Laboratory, where much of the history of the field was written.

With support from the leading manufacturers and consultants, this program features the broadest experience available anywhere in the fields of high-speed film and high-speed electronic imaging capture and analysis. Of particular interest is the latest trend of merging high-speed electronic images and instrumentation data for in-depth analysis of mechanical events.

The scope of the program should make it invaluable to anyone who wishes to broaden their capabilities in the field of high-speed imaging.

Machine Learning for Big Data and Text Processing: Foundations

June 18-19, 2018

Machine learning methods drive much of modern data analysis across engineering, sciences, and commercial applications. For example, search engines, recommender systems, advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. Much of today's data is available in primarily textual form, requiring effective tools for using unstructured and semi-structured text. This course examines a suite of key machine learning tools and their applications, including predictive analysis. We will discuss key insights underlying the tools, what kinds of problems they can/cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications.

The course is designed to operate simultaneously on two levels, intuitive and more formal, describing key concepts, formulations, algorithms, and practical examples for professionals whose work interfaces data analysis in different ways and on different levels.

  • At the managerial level, the course provides the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data.
  • For professionals whose work involves data hands-on, the course aims to provide a deeper understanding and sharper intuitions about what is possible, what is not, and which methods to consider in what contexts.
  • For everyone, the course provides the ability to see problems as machine learning problems and be able to discuss ways to approach them.

Learning Objectives

  1. Understand broad opportunities for automation with machine learning
  2. Be able to formulate/set up problems as machine learning tasks
  3. Outline key aspects of practical problems that are likely to impact performance
  4. Assess which types of methods are likely to be useful for a given class of problems
  5. Understand strengths and weakness of "on-line" learning algorithms
  6. Be able to discuss scaling issues (amount of data, dimensionality, storage, and computation)
  7. See through the process of applying machine learning methods in practice, foresee likely hurdles and possible remedies
  8. Understand modern natural language processing tools, formulations, and problems
  9. Grasp what predictive analytics often does not provide
  10. Understand current machine learning trends and opportunities that they bring

Organizations, Innovation, and Technology: Putting Ideas to Work

June 18-22, 2018

Innovation typically begins with a new technical concept or other bright idea. But the new idea is just the first step on the long path to successful innovation. Technical change usually requires organizational changes as well. These changes include providing resources for technical development and acquiring the support of others in the organization or in outside organizations. Gaining this support requires negotiation, bargaining, and coalition building. Organizational change, then, is a very complex process. Change of this sort can be very difficult. Significant innovations can be resisted, fall victim to competing ideas, or fail to be sustained.

Thus innovators need their original idea and a vision of how the world will change if the innovation succeeds. But the real bottleneck in achieving success is the organizational change needed for implementing that idea. This course focuses on strategies to overcome the bottlenecks: how to build the needed coalition of supporters who will enable the necessary organizational change. This change process is not captured by simple cookbook procedures, so we will investigate a variety of detailed, original case studies, rich in lessons for innovation success and failure. The cases are drawn from many sectors, public and private, from the U.S. and other countries. We will also explore the diversity of innovation experiences of the class participants.

In evaluating organizational innovation strategies, there are obvious differences between public and private sector organizations. Yet while the incentives are often very different, the underlying processes of innovation are very similar in the two sectors. We are particularly interested in public - private interactions. Successful innovation strategies in the private sector often involve effective exploitation of public organizations, while public innovation usually requires mobilization of support from the private sector.

Who Should Attend

Private and public managers, consultants, and academics who are working to promote and sustain innovations through organizational change.

Other typical participants came from Samsung, Corning, Shell, Intel, Siemens, Northrop Grumman, Monsanto, Toyota, Halliburton, EMC, the Office of the Secretary of Defense and numerous military labs, Allianz SE, MITRE, Akamai Technologies, the CIA, Deere & Co., Booz Allen Hamilton, the European Commission, and other firms, government agencies, and universities around the world.

Rapid Prototyping Technology

June 18-22, 2018

Participants will obtain hands-on exposure to processes commonly used to rapidly fabricate prototypes. Classroom time covers an introductory-level review of the principles that govern the technologies, design for manufacturing, and best practices. Laboratory time includes design of representative components, observation of fabrication by MIT staff, and measurement/inspection of the resulting parts. The course materials cover 3D printing, laser cutting (polymers), waterjet cutting (metals and polymers), CNC milling (metals and polymers), CNC turning (metals and polymers), thermoforming (polymers), silicone molding, and use of a CNC router (wood and/or foam).

Who Should Attend

This course is directed at individuals who need to understand what dominates and limits the capabilities of the rapid prototype fabrication processes that will be covered in the class, and is designed for professionals that are looking to gain knowledge and insight that enables them to select appropriate processes/technologies and then make good design/fabrication/assembly decisions when utilizing the processes. The course would be useful for individuals seeking to better understand how to prototype items, for example: designers, design engineers, directors of engineering, technicians, researchers, makers, model builders, and hobbyists. The lessons learned are highly useful in fields related to design, manufacturing, the arts, architecture, and R&D.

Prerequisite Skills/Knowledge

A technical background including trigonometry and freshman-level physics will enable participants to more fully understand the principles, i.e. the "why," of that which governs the capabilities and limitations of the processes.

Advanced Machine Learning for Big Data and Text Processing

June 20-22, 2018

Machine learning methods drive much of modern data analysis across engineering, science, and commercial applications. For example, search engines, recommender systems, advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. This course looks at how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied in different fields, even when using unstructured data. You'll gain insights about the underlying tools, what kinds of problems they can/cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications, particularly in the healthcare field.

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