WPI Data Science researchers are exploring every aspect of this burgeoning field. Together, they innovate Data Science techniques and technologies, and their applications fuel breakthroughs that have direct, real-world impact. These state-of- the-art analytics tools allow users to explore data spurring advances in digital health, genetic analysis, educational software, financial trading, and more. Talented graduate students also have frequent opportunities for paid research positions, fellowships and industrial sponsorships. The students are privileged to participate in big data research projects and gain in-the- field experience, learning to turn raw data into actionable information.

Research Focus Areas

Faculty across disciplines work on shared research projects in data science areas as diverse as:

  • Big data and high performance analytics
  • Bioinformatics and genomic databases
  • Business intelligence and predictive analytics
  • Cryptography and cyber security
  • Data mining and knowledge discovery
  • Educational data mining
  • Financial decision-making
  • Healthcare data analytics
  • Internet big data analytics
  • Large-scale data management and infrastructures
  • Numerical and statistical data analysis
  • Optimization and prescriptive analytics
  • Signal processing and information theory
  • Social media analytics
  • Statistical and machine learning
  • Visual analytics of large data sets

Visualizing MBTA Data: An Interactive Exploration of Boston's Subway System

As part of Professor Matthew Ward's Data Visualization class, two of WPI’s brightest students, Mike Barry and Brian Card, worked on a fascinating interactive visualization project.

The exciting project, to analyze and display data released by Boston's Massachusetts Bay Transit Authority (MBTA) about its subway and real-time train location data was especially noteworthy after the winter of 2015! If you have ever ridden a subway train, anywhere in the world, you’ll love this project!

Their visual analytics tool helps people in Boston better understand transit and commute times in and out of the city.  The MBTA Visualization Project is a prime example of the power that Visual Data Analytics can offer in helping us make sense of and understand data. The MBTA Data Project is an especially exciting one as it allowed us to interactively explore public data; data that has a fundamental effect on the daily lives of Boston public transit commuters. 

Massachusetts Technology, Talent, and Economic Reporting System (MATTERS)

Nearly two dozen undergraduate, master’s, and Ph.D. students in WPI’s Data Science Program collaborated for 2 years, under the guidance of Professor Rundensteiner, to develop a novel data analytics system that could help shape economic policy in Massachusetts.

The Massachusetts Technology, Talent, and Economic Reporting System (MATTERS) is an online analytics dashboard empowered by a powerful dynamic data integration infrastructure. Extracting data sets across various public government data sites, the system allows users to quickly access, analyze and visualize a number of key factors impacting the economic competitiveness of US states.

This project is a collaboration between the Massachusetts High Technology Council (MHTC) and Worcester Polytechnic Institute. Under the supervision of Professor Elke Rundensteiner, students at WPI have worked with experts from high tech industry, research organizations, and higher education institutions developing this tool.

Research Profiles

Andrew Trapp

Associate Professor
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Foisie Business School
Teaching has many facets. Something I love about teaching is that it provides the opportunity to share what I've learned. I have a passion for learning, and I can model this passion to students by encouraging critical thinking in the classroom, connecting with students and drawing out their understanding. I believe it's important to demonstrate the relevancy of what I'm teaching, so I try to merge elements of both my research and real-world practice into the course content whenever I can, and I especially enjoy integrating technology when appropriate -- many students seem to enjoy technology.
Elke Rundensteiner

Professor
Director of Data Science
Computer Science
My research focuses on how to make use of data and information in an effective manner, towards achieving goals in business, scientific discovery, health services, or in personal endeavors. With the inter-connectivity of the internet, the availability of computing power, and big data everywhere, access to the right piece of information at the right moment, possibly fused together from numerous information sources, remains one of the most critical capabilities that can set you apart from others.
Randy Paffenroth

Associate Professor
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Mathematical Sciences
My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.
Xiangnan Kong

Assistant Professor
Computer Science
Professor Kong’s research interests focus on data mining and big data analysis, with emphasis on addressing the data variety issues in biomedical research and social computing. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

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.

 

Making Sense of Data Streams on the Fly

Elke Rundensteiner, Ph.D. is working to develop novel techniques for extracting information from large-scale distributed databases on the fly.

 

Finding Patterns that Can Improve Sleep

Carolina Ruiz, Ph.D. develops algorithms related to sleep and lack of sleep that can optimally help us be more rested and refreshed.

 

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.