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:
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-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:
Dr. Dong Hye Ye’s research interests encompass deep learning for image and signal processing, high-throughput microscopic imaging, and medical image analysis including brain MRI, CT reconstruction, and disease classification. Dr. Ye hopes his research will influence his discipline by improving the application of deep learning in medical imaging, which can be challenging due to the limited training data related to high costs of imaging procedures and patient data privacy issues. “Data augmentation with our proposed generative adversarial network can take account into the underlying data distribution in terms of clinical variables such as patient age and disease diagnosis. This will create more balanced and bigger datasets, improving the robustness of trained deep neural networks,” Ye stated.
Ye believes that using the generative adversarial network will help advance the field of data science. “The existing machine learning methods assume that training and testing datasets are under the same statistical distribution. But, in testing, there would be adversarial samples which violate the statistical assumption. By using our generative adversarial network, which takes account into the underlying data distribution, we can deal with the adversarial samples and improve the robustness of the machine learning in real-world applications.” Ye hopes to incorporate adversarial learning into various deep learning models in medical image and image processing such as brain MRI harmonization, pediatric abdominal CT organ segmentation, breast cancer histopathological image classification, and optical coherence tomography/adaptive optics/ fundus photography analysis for eye imaging.
When asked who he’d most like to collaborate with, Dr. Ye commented that he’d like to work with any radiologist, pathologist, and biochemist to solve their domain problems with adapted deep learning methods. “This will help to understand the biochemical characteristic of human tissues, enhance patient care, and ultimately improve people’s lives.”learn more
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.