MachineLearningApplications@CSAIL: Bringing new applications, workforce cohesion and technological vision to industry

This initiative will focus on applications of the latest machine learning (ML) technologies, research on the resolution of current challenges limiting the abilities of ML and professional development that will help prepare a company’s workforce for this digital transformation.

Organizations who know how to leverage and integrate ML across their business will have a competitive advantage. All industries including retail, food/beverage, travel/tourism, household goods, construction, fashion, agriculture, manufacturing/ packaging, education, pharmaceutical, health care and more will all benefit from the latest ML technologies.
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Systems That Learn: Accelerating the development, deployment and evolution of large-scale AI systems

With this Initiative, we combine expertise in systems and machine learning to create new applications for understanding complex relationships from the avalanche of data available today. STL has been created to enable cross-collaboration and accelerate development of innovative human-like systems to serve the world.
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FinTech@CSAIL will bring together industry, thought leaders, innovators, academics, disruptive technology development and start-up companies that are reinventing global financial services.
Through FINTECH@CSAIL, we will work closely with industry partners in leveraging innovation from cutting edge research to develop the next generation of impactful technologies that will open up new business models, broaden access, gain new data insights, and improve security.
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A huge part of our computing future will be wireless and mobile.

To realize that future, CSAIL researchers are developing novel solutions to four challenges: dramatic improvements in spectrum use, systems to develop and run distributed mobile applications at scale, security and privacy in the mobile world, and energy-efficient designs.

These efforts are part of the MIT Center for Wireless Networks and Mobile Computing, housed at CSAIL, whose participants also include researchers from LIDS, MTL, and RLE.
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