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Fermentation Technology

July 23-27, 2018

Fermentation Technology is the longest-run course in the MIT Professional Education catalog, having been offered continuously for more than 40 years. This course emphasizes the application of biological and engineering principles to problems involving microbial, mammalian, and biological/biochemical systems. The aims of the course are to review fundamentals and provide an up-to-date account of current knowledge in biological and biochemical technology. The lectures will emphasize and place perspectives on biological systems with industrial practices.

This course has made some major additions, modifications, and revisions in the course topics and course contents over the past couple of years. In recognition of the increasing number of attendees from non-pharmaceutical industries, we are rebalancing the course to provide equal emphasis on mammalian and microbial technologies. More than half of the lecturers are currently working in industry or have industrial experience.

The course is intended for engineers, biologists, chemists, microbiologists, and biochemists who are interested in the areas of biological systems in prokaryotic and eukaryotic hosts. It is desirable that individuals enrolled be familiar with some of the general aspects of modern biology, genetics, biochemical engineering, and biochemistry. Some general knowledge of mathematics is also desirable for dealing with the engineering aspects of the course.

Modeling and Simulation of Transportation Networks

July 23-27, 2018

Modeling and simulation methods are essential elements in the design and operation of transportation systems. Congestion problems in cities worldwide have prompted at all levels of government and industry a proliferation of interest in Intelligent Transportation Systems (ITS) that include advanced supply and demand management techniques. Such techniques include real-time traffic control measures and real-time traveler information and guidance systems whose purpose is to assist travelers in making departure time, mode and route choice decisions. Transportation researchers have developed models and simulators for use in the planning, design and operations of such systems. This course draws heavily on the results of recent research and is sponsored by the Intelligent Transportation Systems Laboratory of the Massachusetts Institute of Technology.

The course studies theories and applications of transportation network demand and supply models and simulation techniques. It provides an in-depth study of the world's most sophisticated traffic simulation models, demand modeling methods, and related analytical techniques, including discrete choice models and their application to travel choices and driving behavior; origin-destination estimation; prediction of traffic congestion; traffic flow models and simulation methods (microscopic, mesoscopic and macroscopic); and alternative dynamic traffic assignment methods.

This program is intended for analysts, engineers, managers and planners, as well as industry, government and academic researchers who seek to understand, analyze and predict performance of transportation systems. Participants with backgrounds in diverse areas such as traffic engineering, systems engineering, transportation planning, operations management, operations research and control systems are welcome.

Understanding and Predicting Technological Innovation: New Data and Theory

July 23-27, 2018

This course on technological innovation will be organized around three modules on (1) Data, (2) Theory, and (3) Application. In the first module, we will analyze new, large data sets on technological improvement, many of which were collected by the instructor and are the most expansive of their kind. We will cover statistical analysis methods and decomposition models in order to extract useful insight on the determinants of technological innovation. Examples from energy conversion, transportation, chemicals, metals, information technology, and a range of other industries will be discussed. In the second module, we will cover theories, that have been developed in recent years and stretching back several decades, to explain technological innovation. We will cover the disciplinary origins of these theories, the empirical evidence for or against them, and the usefulness of these theories for practitioners from various fields including engineering, chemicals, private investment, and public policy. Building on this insight, in the third module we will focus on applying the data analysis methods and theories covered to inform decisions about technology investment and design. The third module will address questions of specific interest to the class. This module will demonstrate the utility of the material covered and how it can be extended to answer a wide range of important questions relating to investment, research and development, manufacturing, and public policy.

Designing Efficient Deep Learning Systems

July 23-24, 2018

Deep learning is widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, robotics, etc. While deep learning delivers state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, designing efficient hardware systems to support deep learning is an important step towards enabling its wide deployment, particularly for embedded applications such as mobile, Internet of Things (IOT), and drones.

Who Should Attend
This course is designed for research scientists, engineers, developers, project managers, startups and investors/venture capitalists who work with or develop artificial intelligence for hardware and systems, as well as mobile or embedded applications:

* For project managers and investors/venture capitalists whose work involves assessing the viability or potential impact of a deep learning system and selecting a research direction or acquisition, this course aims to provide an overview of the recent trends as well as methods to assess the technical benefits and drawbacks of each approach or solution based on a comprehensive set of metrics.
* For research scientists and engineers whose work involves designing and building deep learning systems, this course aims to provide an overview of the various state-of-the-art techniques that are being used to address the challenges of building efficient deep learning systems.
* For startups and developers whose work involves developing deep learning algorithms and solutions for embedded applications and systems, this course aims to provide the insights necessary to select the best platform for your goals and needs. It will also highlight techniques that can be applied at the algorithm level to improve the energy-efficiency and speed of your proposed solution.

Climate Change: From Science to Solutions

July 30 - August 3, 2018

The objective of this course is to provide participants with a thorough understanding of the scientific foundation behind anthropogenic climate change, its impacts on the Earth, and strategies to address it. The course introduces the fundamental physical processes that shape climate, focusing on the drivers of past, present, and future climate change. Impacts of climate change on the environment and human societies will be highlighted, including effects on temperature, precipitation, ocean acidity, sea level, severe storms, agriculture, biodiversity, and air quality. Mitigation approaches and adaptation strategies, including technology development, will be introduced, discussed, and critiqued. The course will conclude with an overview of policy and governance considerations in a changing climate. Non-lecture activities will comprise 25% of the course, and will address the contemporary science of climate measurements, models of climate change and impacts, and climate change policy and negotiations.

Who Should Attend:
This course is targeted to environmental scientists, engineers, and consultants who seek a deeper understanding of the science of climate change. Professionals in the energy, finance, insurance/risk mitigation, and food/beverage sectors as well as those working in government, NGOs, and education will also have an interest in this topic.

Design and Analysis of Experiments

July 30 - August 3, 2018

Planning Experiments, Doing Experiments, and Analyzing Experimental Data

This one-week program is planned for persons interested in the design, conduct and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield.

Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional factorial designs are discussed in greater detail. These are designs in which two or more factors are varied simultaneously; the experimenter wishes to study not only the effect of each factor, but also how the effect of one factor changes as the levels of other factors change. The latter is generally referred to as an interaction effect among factors.

The fractional factorial design has been chosen for extra-detailed study in view of its considerable record of success over the last thirty years. It has been found to allow cost reduction, increase efficiency of experimentation, and often reveal the essential nature of a process. In addition, it is readily understood by those who are conducting the experiments, as well as those to whom the results are reported.

The program will be elementary in terms of mathematics. The course includes a review of the modest probability and statistics background necessary for conducting and analyzing scientific experimentation. With this background, we first discuss the logic of hypothesis testing and, in particular, the statistical techniques generally referred to as Analysis of Variance. A variety of software packages are illustrated, including Excel, SPSS, JMP, and other more specialized packages.

Throughout the program we emphasize applications, using real examples from the areas mentioned above, including such relatively new areas as experimentation in the social and economic sciences.

We discuss Taguchi methods and compare and contrast them with more traditional techniques. These methods, originating in Japan, have engendered significant interest in the United States.

Applicants need only have interest in experimentation. No previous training in probability and statistics is required, but any experience in these areas will be useful.

All participants receive a copy of the text, Experimental Design: with applications in management, engineering and the sciences, Duxbury Press, 2002, co-authored by Paul D. Berger and Robert E. Maurer, in addition to extensive PowerPoint-style notes.

Downstream Processing

July 30 - August 3, 2018

Continuing discoveries in molecular biology, genetics, and process science provide the foundation for new and improved processes and products in today's biochemical process industry. The production of therapeutic proteins, which is made possible by discoveries in biotechnology, will generate sales exceeding $300 billion in 2016. In addition, biotechnology has led to marked improvement and expansion in the traditional biochemical process industry for production of enzymes, diagnostics, chemicals, pharmaceuticals, and foods. Continued introduction of new technology necessitates innovation in process development scale-up and design. As a consequence, there is the need to design new, as well as to improve existing, processes. An integral and cost intensive part of these processes is associated with downstream processing for product isolation and purification.

Who Should Attend

The course covers fundamental principles of downstream processing with practical examples and case studies to illustrate the problems and solutions faced by the practitioner. It is intended to provide both insight into and an overview of downstream processing for individuals actively engaged in process research and development, as well as those who manage and innovate in the biochemical process industry. Increasingly, scientists and engineers engaged in fermentation and cell culture development attend the course to better understand the context of the whole process. Attendees include:

  • Engineers and scientists interested in design, economics, validation optimization and scale-up of biochemical product recovery;
  • Protein biochemists and chemists involved in design of recovery processes;
  • Managers responsible for biochemical process development;
  • Entrepreneurs, attorneys, and business leaders wanting an overview and insight into biochemical manufacturing.

Product Platform and Product Family Design: From Strategy to Implementation

July 30 - August 3, 2018

This course explores how product architecture, platforms and commonality can help a firm deploy and manage a family of products in a competitive manner. We will examine both strategic as well as implementation aspects of this challenge. A key strategy is to develop and manufacture a family of product variants derived from a common platform and/or modular architecture. Reuse of components, processes and design solutions leads to advantages in learning curves and economies of scale, which have to be carefully balanced against the desire for product customization and competitive pressures. Additionally, platform strategies can lead to innovation and generation of new revenue growth, by intelligently leveraging existing brands, modules, and sub-system technologies. We will present the latest theory as well as a number of case studies and industrial examples on this important topic. We will engage the course participants through interactive discussion and hands-on activities. Recent strategic issues such as embedding flexibility in product platforms as well as the effect of platforms on a firm's cost structure, organization, and market segmentation will also be presented.

This course is targeted towards executive decision makers, product managers, marketing managers, product line strategists, product architects, as well as platform and systems engineers in industrial and government contexts. Such individuals will have to strategically position their products and systems in a competitive marketplace and define modular and scalable product architectures, utilizing standardization, commonalization, customization and platform leveraging strategies to maximize cost savings while increasing the capability to offer a variety of customized systems and products. A basic background in mechanical and/or electrical engineering, as well as some business and accounting experience is beneficial but not required.