Establishing Data Science Thought Leaders

The Northwestern Mutual Data Science Institute is committed to solving important business and community challenges by harnessing the power of data for research and scientific discovery.

Our research efforts are prioritized by using data to address three major themes:

  • Tackling some of the world’s largest health crises
  • Addressing challenging social issues that affect a large population
  • Finding innovative solutions for business and industry problems

Underlying these areas is an unwavering focus on the ethical use of data to ensure we’re on the cutting edge of data science trends and effecting positive change in a responsible manner.


NMDSI Active Research

NMDSI-supported research advances innovative ideas, while fostering collaborations that will lead to long-term, deep relationships among faculty — across disciplines — at both UW-Milwaukee and Marquette. Through these collaborations, we will maximize contributions to data science while producing internationally-recognized data science research and thought leadership. Check out these current active projects:

The Elecurator Project

The project tracks the major issues engaging both candidates and voters in the 2020 presidential election cycle. Using a technique known as social curation, the team is analyzing multiple sources of data to see what topics are important to voters and how the candidates are speaking about those same issues.

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Meet our Researchers

  • Dr. Kristof Kipp
  • Associate Professor
  • Exercise Science
  • Marquette University

Dr. Kristof Kipp’s focus is sports biomechanics - a very applied research discipline that bridges the gaps between research and practice, and therefore often has direct implications for improving performance or preventing injuries.

Kipp’s research agenda focuses on how movement patterns relate to sports performance, injury prevention, and rehabilitation with the long-term goal of helping people move and perform to the best of their ability, without the risk of (re)injury. One of his research goals is to help people improve their movement patterns so that they can perform their sport to the best of their ability without the risk of injury. This goal is accomplished via two methods: 1) training and 2) biofeedback interventions. The focus of the first method is to test whether specific neuromusculoskeletal determinants provide valid targets that, if trained, can improve movement patterns. The focus of the second method is to test whether real-time biofeedback can improve key performance indicators or ameliorate injury risk factors.

Recently, Kipp has been studying the use of low-cost inertial measurement units, mobile apps, and machine vision technology as alternatives to more expensive lab-based motion analysis methods for research in more ecologically valid environments (i.e., the “real world”). He combines these data with computational and analytical approaches to model intra- and inter-individual movement patterns and behaviors in the context of the respective sport and environment.

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Affiliated Faculty

The NMDSI Affiliated Faculty program provides resources and support to data science faculty, tracks data science research and education, and facilitates building expert multidisciplinary teams from faculty with overlapping or complementary skills and interests.

By building a data science community based on collaboration and increasing the collective impact of data science research, the NMDSI Affiliated Faculty program brings us one step closer to achieving our goal of transforming our world through the power of data science.

Interested in joining the NMDSI Affiliated Faculty program? Submit your application below.

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