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Cambridge, MA

Communication and Persuasion in the Digital Age

June 14-15, 2018

Advancements in technology and the rapid proliferation of digital media, data analytics, and online collaboration require executives to lead their organizations with sophisticated communication skills, adapted for these new ways of working. To be a successful leader today, you must be able to effectively persuade and influence at all levels, in person and virtually, and with supporting data.


Edward Schiappa and Ben Shields draw on cutting-edge communication research, theories of persuasion, studies on parasocial interaction, and empirical studies on compelling storytelling to help participants solve problems, make quality decisions, and motivate people. Session topics include speaking persuasively, visual persuasion, communicating quantitative information clearly, and adapting messages to audiences.

The program will help you leverage new communication skills and harness the power of persuasion to:


  • Influence attitudes and change behaviors in your organization
  • Understand how new technology shapes the way we work and communicate
  • Bring your message and your medium into alignment
  • Support your message with data analytics
  • Manage virtual communications with power and presence
  • Apply the latest research to become a confident and inspiring public speaker
  • Create a compelling story to galvanize and motivate people
  • Adapt and deliver your message across different media channels and to diverse audiences
  • Advance the level of discourse within your organization

MIT Campus, Cambridge, Massachusetts

Implementing Improvement Strategies: Dynamic Work Design

June 14-15, 2018

This program provides practical tools and methods for sustainable improvement efforts of any scale, in any industry, and in any function. It is built on a foundation of principles and methods called Dynamic Work Design and can be adapted to any type of work in any type of organization.


Proceeding from principles, not practices, is a key to sustainable change, allowing integration with current culture, processes, and practices, while delivering fast results with little overhead of training or major initiatives. The method has proven to work in businesses as diverse as oil/gas, DNA sequencing and engineering/innovation and works at the scale of discreet problems or organizational-wide strategic efforts. Improvement begins to happen in rapid and natural ways; results begin showing up almost immediately.


This process improvement training program is inspired by the collaboration between instructors Don Kieffer and Nelson Repenning who integrated industry practice and academic investigation over a 20-year period to develop Dynamic Work Design. Students will learn to identify the value-added elements of their own work and of their organization and more importantly, identify opportunities for improving and how to get started based on a framework of principles and methods.

Please note: The subtitle of this program has changed. The program was previously named "Implementing Improvement Strategies: Practical Tools and Methods."

The main purpose of this program is two-fold: one is to help participants understand how continuous improvement strategies, sustained over a long period of time, affect core business metrics and contribute to the success of the organization, from bottom-up and top-down perspectives; and the other is how to change the way managers see work and their own roles as leaders in the culture of improvement. This program will enable participants to:


  • Understand the principles and approaches that drive improvement; and apply them in all areas in the context of a particular company, thus creating a tangible culture of continuous improvement
  • Implement improvement naturally in their everyday work, not from a prescribed list, but from a deep personal understanding of the principles
  • Recognize successful improvement initiatives already in place and build on them
  • Identify the true value-added aspects of work performed by individual workers and the entire organization
  • Ensure that business targets and improvement activities are tightly linked at every level
  • Develop inquiry and evidence-based problem solving skills for individuals and for organizations
  • Transform managers from controllers to enablers by leveraging the relationship between designing the work well and the engagement of employees that follows
  • Generate ?pull? from within the organization for new methods of work
  • Make results (and problems) visible so that they can be addressed constructively
  • Not just remove defects, but learn how to design work correctly from the beginning

Cambridge, MA

Business Dynamics: MIT's Approach to Diagnosing and Solving Complex Business Problems

June 18-22, 2018

In a world of growing complexity, many of the most vexing problems facing managers arise from the unanticipated side-effects of their own past actions. In response, organizations struggle to increase the speed of learning and adopt a more systemic approach. The challenge is to move beyond outdated slogans about accelerated learning and “thinking systemically” to implementing practical tools that help managers design better operating policies, understand complexity, and guide effective change.


This program introduces participants to system dynamics, a powerful framework for identifying, designing, and implementing high-leverage interventions for sustained success in complex systems. It has been used successfully in diverse industries and organizations, such as Airbus, Compaq, General Motors, Hewlett-Packard, Intel, and Merck. Developed at MIT more than thirty years ago by computer pioneer Jay Forrester, system dynamics led to the creation of management flight simulators that allow managers to accelerate learning, experience the long-term side effects of decisions, and design structures and strategies for greater success.


Through intensive, hands-on workshops and interactive experiments, participants will be exposed to the principles of systems thinking and practical methods for putting them into action. They will be introduced to a variety of tools, including mapping techniques, simulation models, and MIT’s management flight simulators, which they can apply to their own business environment as soon as they complete the program. Throughout the week, participants work in small groups and interact closely with the course leaders, Professors Sterman and Repenning.


Participants will experience the Beer Game, a table game, developed by Jay Forrester. Played with pen, paper, printed plastic tablecloths, and poker chips, it simulates the supply chain of the beer industry. In so doing, it illuminates aspects of system dynamics, a signature mode of MIT thought: it illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act.


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

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