Big Data is as big as it is diverse. What exactly is in it? Are there notable trends and patterns? Moreover, are there common patterns in the computations and algorithms applied to Big Data? This talk examines the diverse aspects of Big Data through concrete examples of data, models, and algorithms. We begin by surveying a variety of Big Data sources spanning computational biology, high energy physics, social networks, and cell phone call records. In each case, we discuss the volume, velocity, and structure of the data collection. We then hone in on three case studies and examine the computational and algorithmic requirements for large-scale modeling. This talk provides an in-depth understanding of characteristics of Big Data along with algorithmic pain points of Big Learning, from which we may draw insights about requirements for the next generation of Big Learning tools.
Alice is the Director of Data Science at GraphLab, a Seattle-based startup offering a powerful large-scale machine learning and graph analytics platform. She loves playing with data and enabling others to play with data. She is a tool builder and an expert in Machine Learning algorithms. Her work spans software diagnosis, computer network security, and social network analysis. Prior to joining GraphLab, she was a researcher at Microsoft Research, Redmond. She holds a Ph.D. and a B.A. in Computer Science, and a B.A. in Mathematics, all from U.C. Berkeley.
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