The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power
This talk will summarize some key findings from a new book by Michael A. Cusumano, Annabelle Gawer, and David B. Yoffie. We will focus on the key features associated with digital platforms – businesses that connect two or more market sides, with supply or demand driven at least in part by network effects. Platform companies are now the most valuable companies in the world and the first trillion-dollar businesses. The talk will explain how digital platforms differ from conventional product or service businesses, and why some markets produce spectacular winner-take-all-or-most outcomes while others result in spectacular financial losses.
Customer Analytics
Endor: Predictive analytics on customer data without data scientists FindOurView: Customer insights with language understanding Paradigm Sift: Applying cultural context to understand customers at scale Cerebri.ai: AI-based next actions to drive customer engagement Assetario: Personalizes the in-app purchase to drive user happiness and revenue
Customer Engagement & Technology
GoWith: Enhancing the airport passengers' experience every step of the way. Voomer: AI to empower people for video communication ServiceMob: Making customer service access simple with AI Posh: Conversational AI for customer service & helpdesk Silverthread: Improving software health and economics at scale
For a little over a dozen years, our group has been developing, integrating, and testing various bihormonal (insulin and glucagon) bionic pancreas technologies for autonomous regulation of glycemia in people with type 1 diabetes (T1D). The technology has evolved over the years from a crude and clumsy system of interconnected pumps and sensors cobbled together around a laptop computer, to a system that runs on an iPhone, which wireless communicates with two infusion pumps and a sensor, and, finally, to its ultimate embodiment as a dual-chamber infusion pump, a sensor, and mathematical algorithms all housed within a single compact integrated device, which we call the iLet (in homage to the pancreatic islets of Langerhans which contain the alpha and beta cells that secrete glucagon and insulin).
The laptop version of our bionic pancreas was tested first in a diabetic swine model of T1D at Boston University (BU) between 2005 and 2009 and then in inpatient clinical trials with our collaborators at the Massachusetts General Hospital (MGH) between 2008 and 2012 in adults and adolescents with T1D. Between 2013 and 2016 we conducted outpatient clinical trials of the iPhone version of our bionic pancreas together with our clinical collaborators at MGH, Stanford, the University of North Carolina, and the University of Massachusetts. Results of these studies will be presented along with our plans for the final pivotal trials of the iLet and the pathway ahead for regulatory approval.
2016 MIT Digital Health Conference
Principal Investigator Harvey Sapolsky
Principal Investigator Mitchel Resnick
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.
Principal Investigator Rosalind Picard
Principal Investigator James Utterback
Principal Investigator Ann Graybiel
Principal Investigator Karen Sollins