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.
Faculty across disciplines work on shared research projects in data science areas as diverse as:
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.
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.
I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and foster care.
As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.
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.
Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. 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.
