WPI researchers have a hand in every aspect of data science. Together, our researchers fuel breakthroughs that have direct real-world applications—from creating a data mining system that allows users to explore variables in real time, to applying analytics to spur advances in healthcare delivery, genetic analysis, educational software, financial trading, and more.


PARAS - Parameter Space-based Association Rule Mining

The PARAS technology, released as freeware, takes the guesswork out of determining optimal parameter settings for data mining by enabling interactive rule exploration over big data sets using successive parameter settings in near real time.

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Making Sense of Data Streams On The Fly

Elke Rundensteiner, professor of computer science at Worcester Polytechnic Institute (WPI), is developing novel techniques for extracting information from large-scale distributed databases on the fly.

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Finding Patterns That Can Improve Sleep

Learn from experience is what the algorithms developed by Carolina Ruiz, associate professor of computer science at Worcester Polytechnic Institute (WPI), are designed to do.

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Predictive Analytics for a Smarter Future

Michael Radzicki, professor of social science and policy studies at WPI, mines big data sets to make predictions about future events or behaviors—a process known as predictive analytics.

Some research topics include:

  • computer science researchers create algorithms and infrastructures for storing, extracting insights from, and displaying large quantities of data
  • mathematics researchers build better statistical models for identifying relationships and analyzing multivariate data
  • business researchers leverage data analytics to gather information, improve systems and processes, and make key decisions
  • learning sciences researchers use data mining algorithms to study how students learn and provide data-driven learning technologies
  • humanities and social science researchers investigate how data informs behavior and explore best practices for communicating data
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  • Kong

    Faculty Profile

    Name: Xiangnan Kong
    Department: Computer Science
    Title: Assistant Professor

    Xiangnan Kong’s research involves both data mining and analysis, as he works to discover meaningful patterns in vast amounts of data and extract those patterns to make predictions.

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  • Randy Paffenroth

    Faculty Profile

    Name: Randy Paffenroth
    Department: Mathematics
    Title: Visiting Associate Professor

    Bridging mathematics, computer science, and software engineering, he tackles large scale data analytics from multiple angles—proving theorems and using those theorems to find solutions.

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  • Andrew Trapp

    Faculty Profile

    Name: Andrew Trapp
    Department: School of Business
    Title: Assistant Professor

    Trapp focuses on optimization over large data sets—designing and refining algorithms that find optimal solutions to complex problems using large, and at times incomplete, sets of data.

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