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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.

Data Science and Predictive Analytics Textbook

By | Research

A new textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R provides a solid Data Science foundation and identifies challenges, opportunities, and strategies for designing, collecting, managing, processing, interrogating, analyzing and interpreting complex health and biomedical datasets. It focuses on active-learning by integrating driving motivational challenges with mathematical foundations, computational statistics, and modern scientific inference. The material builds scientific intuition, computational skills, and data-wrangling abilities to tackle Big biomedical and health data problems. The resources include well-documented R-scripts and software recipes implementing atomic data-filters as well as complex end-to-end predictive big data analytics solutions.

The increasing popularity of this textbook, authored by a MIDAS Faculty, Ivo D. Dinov, and part of the Springer Computer Science Series, is evidenced by the number of readers and downloads. Compared to other computer science books, in 2018 this textbook had > 18,000 downloads vs. 14,000 for the average textbook, and for the first quarter of 2019, it had > 4,400 downloads compared to an average of 1,400 downloads. In addition, the book’s website (http://dspa.predictive.space) has 100,000’s of online readers, who have free access to all learning modules, assignments, videos, software tools, and case studies.

Study on bias in learning analytics earns Brooks Best Full Research Paper Award at LAK conference

By | General Interest, Happenings, News, Research

A paper co-authored by University of Michigan School of Information research assistant professor Christopher Brooks received the Best Full Research Paper Award at the International Conference on Learning Analytics & Knowledge (LAK) Conference in Tempe, Arizona. The award was announced on the final day of the conference, March 7, 2019.

The paper, “Evaluating the Fairness of Predictive Student Models Through Slicing Analysis,” describes a tool designed to test the bias in algorithms used to predict student success.

The goal of the paper, Brooks says, was to evaluate whether the algorithms used to predict whether students would succeed in massive online courses (MOOCs) was skewed by the gender makeup of the classes.

“We were able to find that some have more bias than others do,” says Brooks. “First we were able to show that different MOOCs tend to have different bias in gender representation inside of the MOOCs.”

Read more…

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

By | Research


HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

Journal of Statistical Computation and Simulation


11 Nov. 2018


Simeone Marino, Nina Zhou, Yi Zhao, Lu Wang, Qiucheng Wu & Ivo D. Dinov (2019)

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques. To address this problem, we developed a novel statistical obfuscation method (DataSifter) for on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). DataSifter provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Simulation results suggest that DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.