• Big Data promises a better world.  A world where data will be used to make better decisions, from how we invest money to how we manage our healthcare to how we educate our children and manage our cities and resources.  These changes are enabled by a proliferation of new technologies and...
  • The MIT Big Data Challenge    Take me to the CITY OF BOSTON TRANSPORTATION challenge page!  OVERVIEWThe MIT Big Data Initiative at CSAIL is organizing competitions designed to spur innovation in how we think about and use data to address major societal issues.  Big Data...
  • MotivationAs financial markets have undergone rapid transformations in the past two decades due to technological advances, organizations in this sector, including banks, asset management firms, companies managing digital payments, telecoms, IT firms, and governmental and regulatory agencies, are...
  • MIT BIG DATA LIVING LABA key issue today is that data is siloed, whether its personal data, data inside an organization, or data sharing across different organizations. Data discovery and integration is difficult and presents complex technical, organizational and policy challenges. A Living Lab...


    Course Description This Digital Programs course will survey state-of-the-art topics in Big Data, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), visualization, and a range of applications. Each module will introduce broad concepts as well as provide the most recent developments in research. The course is taught by a team of world experts in each of these areas from MIT and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). CSAIL is the largest research laboratory at MIT and one of the world’s most important centers of information technology research. CSAIL and its members have played a key role in the computer revolution. The lab’s researchers have been key movers in developments like time-sharing, massively parallel computers, public key encryption, the mass commercialization of robots, and much of the technology underlying the ARPANet, Internet, and the World Wide Web. CSAIL members (former and current) have launched more than 100 companies, including RSA Data Security, Akamai, iRobot, Meraki, ITA Software, and Vertica. The Lab is home to the World Wide Web Consortium (W3C). With backgrounds in data, programming, finance, multicore technology, database systems, robotics, transportation, hardware, and operating systems, each MIT Tackling the Challenges of Big Data professor brings their own unique experience and expertise to the course. For more information and to register please

    Course begins: 10/6/16

    Register by: 10/13/15

    Register at: http://bit.ly/1M6aGSw


    Big Data Privacy Workshop: Advancing the State of the Art in Technology and Practice
    Co-hosted with the White House Office of Science and Technology Policy
    watch video or view video clips and slides
    MIT BIG DATA privacy workshop summary report

    The goal of the MIT Big Data Initiative, a multi-year effort launched in May 2012, is to identify and develop new technologies needed to solve next generation data challenges that will require the ability to scale well beyond what today's computing platforms, algorithms, and methods can provide.  We want to enable people to leverage Big Data by developing systems and platforms that are reusable and scalable across multiple application domains.

    Our approach includes two important aspects.  First, we will work closely with key industry and government stakeholders to provide real-world applications and drive impact.  Promoting in-depth interactions between academic researchers, industry and government is a key goal.  Second, we believe the solution to Big Data is fundamentally multi-disciplinary.  The team includes faculty and researchers hailing from diverse research backgrounds, including algorithms, architecture, data management, machine learning, privacy and security, user interfaces, and visualization, as well as domain experts in finance, industrial, medical, smart infrastructure, education and science.