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Midwest Big Data Hub transitions to second phase with new NSF award

By | News, Research

The National Science Foundation recently announced a second phase of funding for its regional Big Data Innovation Hub program, which comprises a growing network of partners investing in data and data sciences to address grand challenges for society and science.

As part of a four year, $4 million award, the University of Michigan will collaborate with Indiana University, Iowa State University, the University of Minnesota – Twin Cities and the University of North Dakota to establish priority focus areas for the Midwest Big Data Hub (MBDH). The National Center for Supercomputing Applications at the University of Illinois, Urbana-Champaign will continue to lead the hub.

“The Midwest Big Data Hub community engages dozens of regional and national universities, transdisciplinary scholars and industry partners in tackling complex data-driven challenges, developing unique data science courses and training resources, as well as promoting data-science-for-social-good,” said H. V. Jagadish, director of the Michigan Institute for Data Science (MIDAS) and the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science at U-M.

U-M plays an important role in coordinating efforts across campuses so that researchers can harness the power of big data to address important issues related to: 

  • Advanced materials manufacturing
  • Water quality
  • Big data in health
  • Digital agriculture
  • Smart, connected and resilient communities.

“The University of Michigan is leading in biomedical and health research, as well as development and educational activities, while actively participating in other priority areas like water quality and smart manufacturing,” said Ivo Dinov, U-M professor of computational medicine and bioinformatics, who also serves as the associate director for education at MIDAS. “The Michigan data science and predictive health analytics research and development will focus on developing advanced clinical decision support systems that can be used to diagnose, track and predict the onset of devastating disorders like cognitive or memory decline, mental health and cancer.”

The Midwest hub, which launched in 2015 with support from NSF, serves a 12-state region that comprises Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota and Wisconsin. The hub leads cross-cutting initiatives for broadening participation in data science education, cyberinfrastructure for research data management and cybersecurity issues around big data. By leading initiatives in data science education and workforce development, the hub aims to increase data science capacity within the region. The NSF also funds regional hubs in the Northeast, South and West, which together cover the entire United States.

“Developing innovative, effective solutions to grand challenges requires linking scientists and engineers with local communities,” said Jim Kurose, NSF assistant director for computer and information science and engineering. “The Big Data Hubs provide the glue to achieve those links, bringing together teams of data science researchers with cities, municipalities and anchor institutions.”

Learn more about the Midwest Big Data Hub at midwestbigdatahub.org.

Nia Dowell, MIDAS funded postdoc, awarded Best Paper representing Education for All at AIED2019

By | News, Research

Nia Dowell, who was a postdoc fully funded by the MIDAS holistic modeling of education project working with MIDAS faculty Christopher Brooks and Tim McKay, was the the lead author on the Best Paper representing Education for All at the annual Artificial Intelligence in Educational (AIED2019) conference. This conference is one of the oldest in the field, going back to the late 80’s. Her work is focused on analytics of education and inclusion. The title of the paper is “Promoting Inclusivity Through Time-Dynamic Discourse Analysis in Digitally-Mediated Collaborative Learning”. Read more about the paper.

Dr. Sue Hammoud and the Michigan Center for Single-Cell Genomic Data Analytics team awarded $3.7 million

By | News, Research

Dr. Sue Hammoud and the Michigan Center for Single-Cell Genomic Data Analytics team received grants from the Open Philanthropy Project ($2.5 million) and Chan Zuckerberg Initiative ($1.2 million). 

The Open Philanthropy Project awarded a grant of $2,500,000 over four years to the University of Michigan to support research by Drs. Sue Hammoud and Jun Li on mammalian gamete development in March of 2019. The research would be specifically focused on development of gametes from stem cells.

Progress in this area could eventually enable people with fertility challenges to have children and could eventually help reduce the incidence of a wide variety of high-burden disorders (such as heart disease, chronic pain, depression, and Alzheimer’s disease) and promote other positive outcomes. Dr. Hammoud’s research is amongst the most promising our science team has encountered so far in this field.


The Chan Zuckerberg Initiative (CZI) awarded $1.2 million to Drs. Sue Hammoud, Jun Li, Erica Marsh and Ariella Shikanov at the University of Michigan.  This project will establish a human cell atlas of the female reproductive system, focusing on the ovaries, fallopian tube, and uterus.


Researchers Leverage Data Science to address Critical Illness and Injury

By | Research

Critical illness and injury is a silent epidemic that impacts more than 5.7 million Americans every year.

It has an enormous societal and economic toll, and in recent years, progress in critical care research has grown more reliant on the ability to gather, store, search and analyze big data.

Two teams at the University of Michigan — the Michigan Institute for Data Science (MIDAS) and the Michigan Center for Integrative Research in Critical Care (MCIRCC) — are partnering to find new and innovative ways to monitor, diagnose and treat critically ill and injured patients.

“More and more people are embracing data science tools and techniques to address important challenges in today’s society, ranging from poverty and mobility to health care,” said H.V. Jagadish, MIDAS director and the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science. “Critical care accounts for nearly 40 percent of hospital costs and patient hospital days, so we’re constantly looking for ways to harness data platforms to accelerate research and solutions in this important field.”

MIDAS, established in 2015 as part of the universitywide Data Science Initiative, aims to advance cross-cutting data science methodology and applications, promote the use of data science to benefit society, build a data science training pipeline and develop partnerships with industry, academia and the community. MCIRCC, established in 2014, brings together integrative teams of scientists, clinicians and engineers to develop and deploy cutting-edge solutions that elevate the care and outcomes of critically ill and injured patients.

MIDAS and MCIRCC have coupled their collaboration with external funding to catalyze several multidisciplinary research projects.

In November 2018, U-M mathematician Harm Derksen and a team of researchers from MIDAS and MCIRCC secured a $1.4 million grant from the National Science Foundation. With federal support, U-M researchers are working to design efficient, numerically stable and computationally feasible algorithms for tensor analysis that could be relevant to a wide range of big data applications, including the treatment of sepsis. This project also will help MIDAS develop new interdisciplinary courses on big data.

“The role of data science in health care is rapidly growing,” said Kayvan Najarian, a professor of computational medicine and bioinformatics who also serves as associate director of MIDAS and MCIRCC. “No longer an exotic or novel approach, it is quickly becoming another tool in the toolbox for researchers and clinicians, a methodology deployed deliberately to serve a defined research need.”

Najarian and Derksen, along with MCIRCC Executive Director Kevin Ward, also partnered to help develop the Analytic for Hemodynamic Instability, which was licensed to Fifth Eye, Inc. in 2017 and has since raised $11.5 million in Series A funding. Using analytics from a single streaming EKG lead, the tool can predict if a patient will deteriorate several hours before normal vital signs signal a problem is occurring.

“Projects like these highlight the importance of using data science to help patients by providing insights on the challenges they face and when to take action to meet them,” said Ward, a professor of emergency medicine who also is a member of the MIDAS executive committee.

U-M SI and MIDAS faculty Ceren Budak among first to study Facebook data

By | Research

Ceren Budak, U-M SI assistant professor and MIDAS researcher, is among one of the first research teams to have access to anonymous data from Facebook. She will be studying social media’s impact on democracy in the United States. The study will look at how sharing behaviors on Facebook are affected by changes Facebook makes to the platform. More information can be found here: https://www.si.umich.edu/news/university-michigan-researcher-among-first-study-facebook-data.

The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance

By | Research

This research was supported by funding from the Michigan Institute for Data Science.

The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance

Published in
ACM, June 23-26, 2019


Sang Won Lee, Aaron Willette, Danai Koutra, Walter S. Lasecki

Facilitating audience participation in a music performance brings with it challenges in involving non-expert users in large-scale collaboration. A musical piece needs to be created live, over a short period of time, with limited communication channels. To address this challenge, we propose to incorporate social interaction through mobile music instruments that the audience is given to play with, and examine how this feature sustains and affects the audience involvement. We test this idea with an audience participation music system, Crowd in C. We realized a participation-based musical performance with the system and validated our approach by analyzing the interaction traces of the audience at a performance. The result indicates that the audience members were actively engaged throughout the performance, with multiple layers of social interaction available in the system. We also present how the social interactivity among the audience shaped their interaction in the music making process.