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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.

Rapid Discovery of Predictive Models from Large Repositories of Signals Data

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