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The goal of SystemsThatLearn@CSAIL membership is to promote in-depth interactions between industry and academia. Member companies will have the opportunity to be exposed to multiple research projects that span the full spectrum of machine learning/artificial intelligence and analytics. We will collaborate closely with industry to provide real-world applications and drive impact. Our team of world-class researchers covers the full spectrum of research in systems and machine learning.

The next decade will usher in a new frontier of sophisticated systems that perform complex “human-like” tasks, with complex inferences and predictions. With data gathered from diverse sensors and mobile devices, computing power spread across embedded devices and datacenters, as well as ubiquitous network connectivity, we will need new tools to realize the potential of learning systems. We are already seeing practical applications of these systems in areas such as autonomous vehicles and personalized health care that have the potential to transform industries and societies.

The goal of the SystemsThatLearn@CSAIL initiative is to accelerate the development of systems and applications that learn.  We intend to accomplish this goal through combining our expertise in Systems and Machine Learning to create new applications for understanding complex relationships from the avalanche of data available today.  SystemsThatLearn@CSAIL has been created to to enable cross-collaboration and accelerate development of innovative human-like systems to serve the world.

Our work will leverage many world-class faculty in MIT Computer Science and Artificial Intelligence Laboratory, who have pioneered the field of machine learning and systems, towards advancing the state-of-the-art with select industry partners to address the hardest real-world business problems. MIT is uniquely positioned to accomplish this, through the rigorous research of our faculty coupled with our tradition of collaborating with industry to ensure our work is both relevant and practical. We anticipate many opportunities for direct, active collaboration and knowledge sharing though events, projects and directed research.

Initiative Members