Today’s consumers want to know more about the goods they purchase and where they come from than ever before. Concerns over issues like fair trade and sustainability are driving many companies, from fledgling startups to industry mainstays, toward radical transparency around sourcing, yet this move isn’t just about brand management. Case studies from the apparel, food, and electronics industries reveal the benefits of better visualization and greater transparency for the whole supply chain, because you can’t improve what you can’t see — but it can still cost you.
Manufacturing and production of invented products have been demonstrated as key to a sustained innovation ecosystem. Here, we will discuss recent analysis of local, regional, and national activities that support manufacturing innovation and workforce development. This includes U.S. efforts including the National Network for Manufacturing Innovation and Advanced Technological Education programs that can reduce the barrier to commercialization of new and important technologies, as well as contrasting efforts in other regions of the world.
MIT Media Lab founder Nicholas Negroponte takes you on a journey through the last 30 years of tech. The consummate predictor highlights interfaces and innovations he foresaw in the 1970s and 1980s that were scoffed at then but are ubiquitous today. And he leaves you with one last (absurd? brilliant?) prediction for the coming 30 years.
Growing evidence supports a critical role of metal-coordination complex crosslinking in soft biological material properties such as underwater adhesion and self-healing. Given their exploitation in such desirable material applications in nature, bio-inspired metal-coordinate complex crosslinking no doubt provides unique possibilities to further advance synthetic polymer materials engineering. Using bio-inspired metal-binding polymers, initial efforts to mimic these material properties have shown promise. In addition, novel opportunities for new fundamental insights on how hierarchical polymer network mechanics can be strongly coupled to supramolecular crosslink dynamics are also emerging. Early lessons from studies of these hierarchical chemo-mechanical couplings will be presented.
Understanding the brain could lead to new kinds of computational algorithms and artificial intelligences, as well as treatments for intractable disorders that affect over a billion people worldwide. However, the brain is a very complex, densely wired circuit, and understanding how it works has remained elusive. In order to map how these circuits are organized, and control their complex dynamics, we are building new tools, which include methods for physically expanding brain circuits so that we can see their building blocks, as well as molecules that make neural circuits controllable by light. Through these tools we aim to enable the systematic analysis and repair of the brain.
From flexible hybrid electronics, to integrated photonic devices, to functional fibers to smart manufacturing, the new U.S. Federal government institutes are advancing new manufacturing technologies and accelerating the pace of manufacturing innovation. This session will review MIT's involvement in these institutes and discuss opportunities for industry partners to participate in developing new products and capabilities.
Despite billions invested in cancer research, our understanding of the disease, treatment, and prevention remains limited. Natural language processing (NLP) can offer new insights by mining the rich but underutilized information encoded in physicians’ observations and clinical findings, which are still primarily recorded as free-form text. NLP-based models can make a difference in clinical practice by improving models of disease progression, preventing over-treatment, and narrowing down on a cure.
This talk is focused on the methods and technologies to answer the question ‘Why does it take a long time to process, analyze and derive insights from the data?’ Dr. Veeramachaneni is leading the ‘Human Data Interaction’ Project to develop methods that are at the intersection of data science, machine learning, and large scale interactive systems. With significant achievements in storage , processing, retrieval, and analytics, the answer to this question now lies in developing technologies that are based on intricately understanding the complexities in how scientists, researchers, analysts interact with data to analyze, interpret, and derive models from it. In this talk, Dr. Veeramachaneni will present how his team is building systems to transform this interaction for the signals domain using an example of physiological signals. Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months.
In this talk, he will describe a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining predictive models from these waveforms. BeatDB radically shrinks the time an investigation takes by: (a) supporting fast, flexible investigations by offering a multi-level parameterization, (b) allowing the user to define the condition to predict, the features, and many other investigation parameters (c) pre-computing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates.
2016 MIT Digital Health Conference
Can data series from a broad patient population be relevant and reliable tools in predicting individual outcomes when compared to personal wellness sensor data? Or, simply put from a patient perspective, “Can what happen to them, happen to me?” Retrieving and making use of “like-me” signal data based on similarity presents challenges far beyond digital marketing’s effectiveness in making targeted book and movie recommendations. By investigating and understanding those unique challenges, our research group has developed an approach based upon locality sensitive hashing (LSH). We will provide an update on our progress towards adapting LSH for fast and accurate Signal Like-Me capability.