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[DEC 2014] Online Tracking and Privacy: summary of lecture by Arvind Narayanan

“Reverse Engineering Online Tracking for Privacy, Transparency, and Accountability”

-- Summary of Lecture by Arvind Narayanan, Assistant Professor, Princeton University [December 8, 2014]

[OCT 2014] Beyond Data Lakes: The DataHub

Organizations are scrambling to capitalize on Big Data for business analytics. By necessity, they’ve developed some creative new data management ideas. Data lakes is one of these.

[OCT 2014] Living Lab: A recap of the our experience at Hack MIT

 Last week, Kelly, Guy, and I attended HackMIT – one of the most prestigious hackathons in the world – as sponsor judges for the Big Data Initiative  at MIT CSAIL. There, we listened to speeches, mentored students, and (importantly) encouraged students to hack on our newly released wifi data. Oh.

New Report - Big Data and Health: Revolutionizing Medicine and Public Health

Dec 2013.  New report on Big Data and Health released this week at World Innovation Summit for Health in DOHA.

Link to report:


As part of the MIT CSAIL bigdata lecture series, Dr. Calvin Andrus who works in Office of the Chief Information Officer at the Central Intelligence Agency, gave a talk on the challenges associated with deploying enterprise-scale analytic engines on top of cloud-based bigdata holdings in a classified environment.

Big Data: The Management Revolution

Erik Brynjolfsson, the Director of the MIT Center for Digital Business, the Schussel Family Professor at the MIT Sloan School, and a Research Associate at the National Bureau of Economic Research was the last speaker in the Big Data Lecture Series - Fall 2012.

Taming Big Data with Berkeley Data Analytics Stack (BDAS), Ion Stoica, Berkeley

As part of the Big Data Lecture Series — Fall 2012, Ion Stoica, a Professor at the EECS Department at University of California, Berkeley gave a talk on BDAS (Berkeley Data Analytics Stack). BDAS is an open-source data analytics stack for complex computations on massive data which supports efficient, large-scale in-memory data processing, and allows users and applications to trade between query accuracy, time, and cost.

What Makes Big Visual Data Hard? Alyosha Efros, CMU

Alyosha Efros, an associate professor at the Robotics Institute and the Computer Science Department at Carnegie Mellon University gave a talk in the Big Data Lecture Series --  Fall 2012 at MIT CSAIL. In his talk he discussed how data-driven techniques can make use of big visual data to tackle computer vision problems which are very hard to model parametrically.

Using Big Data to Shape Empirical Decision Making in Insurance Applications, Murli Buluswar, AIG

Murli Buluswar, Chief Science Officer for AIG-Property & Casualty gave a talk in the Big Data Lecture Series --  Fall 2012 at MIT CSAIL. In this talk he shared details on how the newly created Science Office in AIG  is starting to shape empirical based decision making in a broad suite of areas including underwriting, claims, marketing, sales, and even human resources. His entire talk revolved around the theme of applying data science to influence  business decisions.

"Programming and Debugging Large-Scale Data Processing Workflows" Chris Olston, Google Inc

As part of the Big Data Lecture Series -- Fall 2012, Google’s Chris Olston gave a talk on how to manage processing of large data sets. In this talk he gives an overview of his work on large-scale data processing at Yahoo! Research. He begins his talk by introducing two data processing systems: Pig, a dataflow programming environment and Hadoop-based runtime, and Nova, a workflow manager for Pig/Hadoop. Rest of the talk focuses on debugging, and looks at what can be done before, during and after execution of a data processing operation.


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