Common Searches:
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.
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.
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.
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 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.
