The DePaul Center for Data Mining and Predictive Analytics (DaMPA) is a research, educational and collaborative center that focuses on the applications of data science. The center is a joint venture incorporating faculty from several colleges and disciplines across DePaul University. The research focus of the Center is the development of innovative tools and methods in data mining, predictive analytics, and machine learning, as well as the application of these techniques in areas such as intelligent Web systems, social computing, business intelligence, healthcare, hospitality, marketing, image analysis, and more. The Center seeks partnerships with industry as well as educational and non-profit organizations to plan and conduct joint analytics projects that provide graduate students with an invaluable opportunity to work on real-world problems and to gain problem-solving experience in data science.

Dampa Faculty

Dampa Faculty

List of Faculty & Research Projects

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DePaul Faculty Eli Brown to Present at Midwest Big Data

DePaul Faculty Eli Brown will be speaking at an NSF Midwest Big Data Hub sponsored workshop: Midwest Big Data Opportunities and Challenges (MBDOC). This workshop is designed to bring together junior researchers from top universities and industry leaders to discuss active areas of research and development in Big Data. The primary goals will be to […]

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DePaul Faculty receives two grants from National Science Foundation

National Science Foundation collaborative grant entitled “Asked and Answered: Intelligent Data Science for Software Projects” will support development of a tool that can answer software analytics queries over systems engineering project data (e.g., requirements, design, code, test cases and fault logs). This project is done in collaboration with Dr. Jane Huang from University of Notre […]

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DePaul Faculty a panel member at Analytics Symposium event

The University of Chicago Illinois hosted the CRIM Analytics Symposium on March 31st, 2016, and Dr. Suzanne Fogel of DePaul University was a member of a panel that discussed industry – academic partnerships. The other speakers were Richard Rodts from IBM, Sema Barlas from the University of Chicago, Jim Foster from Archer Daniels Midland, and […]

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DePaul Faculty receives Digital Humanities Start Up Grant

The National Endowment for the Humanities recently awarded a grant to DePaul faculty and staff in March of 2016 for a research project involving the Chicago Public Library, as part of the NEH Office of Digital Humanities Start-Up Grant program. The project will be led by Dr. John Shanahan and Dr. Antonio Ceraso from the […]

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Interview: David Kasik, Boeing on Data Analysis vs Data Analytics

By Anmol Rajpurohit, @hey_anmol David Kasik is Boeing‘s Senior Technical Fellow in visualization and interactive techniques. He is pursuing new ways of using visualization for huge amounts of both geometric and non-geometric data.His work with geometric data made Dave a pioneer in interactive 3D computer graphics. He devoted his first 11 years at Boeing to […]

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GE NFL Head Health Challenge

GE and NFL will award up to $10 million for improved technologies and methods that enable more accurate diagnoses of mild brain injury and prognosis for recovery following acute and/or repetitive injuries. GE-NFL Head Health Challenge: Methods for Diagnosis and Prognosis of Mild Traumatic Brain Injuries. General Electric and the National Football League have established […]

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Analyzing Analysts to Build Better Analysis Software

Our study how analysts used Mode led to major updates designed to fit how data analysts and business analysts actually use data – there’s no one-size-fits-all tool and analysis doesn’t end with the analyst. By Benn Stancil (Mode Analytics). Seven months ago, we launched a public beta of Mode, a tool for analysts to create […]

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10 things statistics taught us about big data analysis

By Jeff Leek, (@jtleek) In my previous post I pointed out a major problem with big data is that applied statistics have been left out. But many cool ideas in applied statistics are really relevant for big data analysis. So I thought I’d try to answer the second question in my previous post: “When thinking […]

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