$900k NSF Grant to Predict Heart Disease, Diabetes Using Machine Learning

Researchers from the College of Engineering and Boston Medical Center (BMC) will use a three-year, $900,000 grant from the National Science Foundation to develop and pilot a health informatics system to predict patients at risk of heart disease or diabetes, and enable early intervention and personalized treatment. Click here to read more.

Robotics and AI Event

The Center for Information and Systems Engineering (CISE) partnered with Greg Woolf, CEO of Coalesce.Info and moderator of The Cognitive Computing Group of Boston, to host a community evening focused on Robotics & AI research at Boston University, on February 28, 2017.

Click here to read more.

BBC News – Tomorrow’s cities

Real-time alterations to the red-and-green cycle can cut congestion time by up to 50% and make a city drive much more agreeable, says Prof Christos Cassandras, a smart cities expert from Boston University, who helped develop the system.

“We have all been in the situation where we keep getting stuck behind red light after red light, so imagine if we can control the traffic lights or even the car to alert drivers that if they accelerate a little bit they will make that green light,” he says.

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CDC 2016

Plenary Speaker at 55th IEEE Conf. Decision and Control

Abstract: Smart Cities are an example of Cyber-Physical Systems whose goals include improvements in transportation, energy distribution, emergency response, and infrastructure maintenance, to name a few. One of the key elements of a Smart City is the ability to monitor and dynamically allocate its resources. The availability of large amounts of data, ubiquitous wireless connectivity, and the critical need for scalability open the door for new control and optimization methods which are both data-driven and event-driven. The talk will present such an optimization framework and its properties. It will then describe several applications that arise in Smart Cities, some of which have been tested in the City of Boston: a “Smart Parking” system which dynamically assigns and reserves an optimal parking space for a user (driver); the “Street Bump” system which uses standard smartphone capabilities to collect roadway obstacle data and identify and classify them for efficient maintenance and repair; adaptive traffic light control; optimal control of connected autonomous vehicles. Lastly, to address the “social’’ dimension, the talk will describe how a large traffic data set from the Massachusetts road network was analyzed to estimate the Price of Anarchy in comparing “selfish” user-centric behavior to “social” system-centric optimal traffic routing solutions.

Click here to read more and watch the lecture video

Click here for the presentation (PDF).