Welcome to the Center for Computational Learning Systems (CCLS)
The Fu Foundation School of Engineering & Applied Science,
Columbia University, New York City
Is it possible to use automated methods
to substitute for software engineering?
Modern computational learning systems can identify and understand complex relationships and causalities in large masses of data in order to predict results and produce crucial outcomes for business, industry and academic research. Vast amounts of data can be analyzed in a few minutes, hours or days that would take individual researchers years to analyze and understand, if at all possible.
Is it possible to predict equipment failure
using automated systems?
We seek to identify challenges where our technologies can make a dramatic difference, especially in areas that clearly benefit society. These opportunities span a wide range of fields, including energy, life sciences, finance, text and the Web, and government. But no matter what the area, CCLS projects share a common mission: to advance the science of learning and apply learning technologies to masses of data to achieve understanding, predictability and optimal outcomes. Both our learning systems and their outputs are valuable deliverables.
Can we discover useful causal patterns
in large amounts of data?
We use a team-based approach. Based in Columbia University’s Fu Foundation School of Engineering and Applied Science (SEAS), we assemble a multidisciplinary team of research scientists, industry specialists and domain experts for each project.