Many poor healthcare outcomes and the majority of wasted healthcare spending can be attributed to bad decision making. It is widely accepted that decision support systems are needed to address this issue, and that machine learning has a key role to play in constructing such systems. However, learning to predict the impact of care decisions is made challenging by the need to scale out to complex populations being managed for complex diseases across complex care networks. We will present some recent work that addresses these challenges.
The next generation of energy storage, sensors and neuromorphic computer logics in electronics rely largely on solving fundamental questions of mass and charge transport of ionic carriers and defects in materials and their structures. Here, understanding the defect kinetics in the solid state material building blocks and their interfaces with respect to lattice, charge carrier types and interfacial strains are the prerequisite to design novel energy storage, sensing and computing functions. Through this presentation basic theory and model experiments for solid state oxides their impedances and memristance, electro-chemo-mechanics and lattice strain modulations is being discussed as a new route for engineering material and properties on the examples of solid state batteries, environmental CO2 sensors and memristors for memory and neuromorphic computing chips. Central are the making of new oxide film materials components, and manipulation of the charge carrier transfer and defect chemistry (based on ionic and electronic carriers), which alter directly the device performances and new operation metrics.
In this talk, I will present an overview of my research in the past decade on large scale optimization for machine learning and collective behavior in networked,natural, engineering, and social systems. These collective phenomena include social aggregation phenomena as well as emergence of consensus, swarming, and synchronization in complex network of interacting dynamic systems such as mobile robots and sensors. A common underlying theme in this line of study is to understand how a desired global behavior can emerge from purely local interactions. The evolution of these ideas into social systems has lead to development of a new theory of collective decision making among people and organizations. Examples include participation decisions in uprisings, social cascades, investment decisions in public goods, and decision making in large organizations. I will investigate distributed strategies for information aggregation, social learning and detection problems in networked systems where heterogeneous agents with different observations (with varying quality and precision) coordinate to learn a true state (e.g., finding aggregate statistics or detecting faults and failure modes in spatially distributed wireless sensor networks, or deciding suitability of a political candidate, quality of a product, and forming opinions on social issues of the day in social networks) using a stream of private observations and interaction with neighboring agents. I will end the talk with a a new vision for research and graduate education at the interface of information and decision systems, data science and social sciences.
This lecture will detail the creation of ultrasensitive sensors based on electronically active conjugated polymers (CPs) and carbon nanotubes (CNTs). Conceptually a single nano- or molecular-wire spanning between two electrodes would create an exceptional sensor if binding of a molecule of interest to it would block all electronic transport. Nanowire networks of CNTs modified chemically or in composites with polymers provide for a practical approximation to the single nanowire scheme. Creating chemiresistive and FET based sensors that have selectivity and accuracy requires the development of new methods. I will discuss covalent and non-covalent medication of CNTs with groups that impart selectivity for target analytes. This can involve reactions at the CNT sidewalls and rapping of the CNTs with CPs. Highly specific chemical processes orthogonal responses can be produced for mixtures of analytes through careful integration of chemical functionality. A prevailing problem in all chemiresistive schemes, which is seldom highlighted by researchers, is drift. This is intrinsic for systems that need to interface with their surroundings and changes in the position of ions of small changes in the organization of the CNTs relative to each other, the electrodes, or their surroundings can change the base resistance. I will detail different methods designed to lock the CNT networks in place. These novel compositions are also designed to accommodate functionality and I will demonstrate how we can use a diversity of transition metals to create selective responses to gases. We will also show that this scheme creates CNT networks that are robust enough for solution sensing and demonstrate chemiresistive based glucose sensing. I will also briefly discuss the successful use of CNT based gas sensors for the detection of ethylene and other gases relevant to agricultural and food production/storage/transportation and integrated systems that increase production, manage inventories, and minimize losses.
In June of this year, MIT will complete the construction of the MIT.nano, an 18,000 sq.m. facility in the middle of the campus for MIT’s nanotechnology-related activities. This facility is, in effect, an acknowledgement of the nanotech’s importance today. Within MIT.nano, SENSE.nano is its first Center of Excellence. The impetus for SENSE.nano is the recognition that novel sensors and sensing systems are bound to provide previously unimaginable insight into the condition of individuals, as well as the built and natural world, to positively impact people, machines, and environment. Advances in nano-sciences and nano-technologies, pursued by many researchers at MIT, now offer unprecedented opportunities to realize designs for, and at-scale manufacturing of, unique sensors and sensing systems, while leveraging data-science and IoT infrastructure.
Moderator: Scott Kirsner, Editor & Co-Founder, Innovation Leader Panelists:
Moderator: Leon Sandler, Executive Director, MIT Deshpande Center for Technological Innovation Panelists:
Moderator: Marcus Dahllöf, Program Director, MIT Startup Exchange Panelists:
As autonomous systems move out of the research laboratory into operational environments, they require ever deeper connections to their surroundings. Traditional notions of full autonomy have led to “clockwork” approaches where robots must be isolated from their human surroundings. Instead, we need precise, robust relationships with people and infrastructure. This situated autonomy appears in driverless cars' dependence on human-built infrastructure, the need for new systems of unmanned traffic management in the air, and the increasing importance of collaborative robotics in factories. How can we best design such systems to inhabit and enhance the human world? In this talk, David Mindell sketches a number of these emerging scenarios, traces new technologies to address the problems they raise, and envisions new approaches to human and robotic interaction that helps people and robots work together safely and collaboratively.