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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.

www.openphilanthropy.org/focus-area/scientific-research/transformative-basic-science

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.

chanzuckerberg.com/science/programs-resources/humancellatlas/seednetworks/human-cell-atlas-of-the-female-reproductive-system/

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…

Women in HPC launches mentoring program

By | Educational, General Interest, HPC, News

Women in High Performance Computing (WHPC) has launched a year-round mentoring program, providing a framework for women to provide or receive mentorship in high performance computing. Read more about the program at https://womeninhpc.org/2019/03/mentoring-programme-2019/

WHPC was created with the vision to encourage women to participate in the HPC community by providing fellowship, education, and support to women and the organizations that employ them. Through collaboration and networking, WHPC strives to bring together women in HPC and technical computing while encouraging women to engage in outreach activities and improve the visibility of inspirational role models.

The University of Michigan has been recognized as one of the first Chapters in the new Women in High Performance Computing (WHPC) Pilot Program. Read more about U-M’s chapter at https://arc.umich.edu/whpc/

Balzano wins NSF CAREER award for research on machine learning and big data involving physical, biological and social phenomena

By | General Interest, Happenings, News, Research

Prof. Laura Balzano received an NSF CAREER award to support research that aims to improve the use of machine learning in big data problems involving elaborate physical, biological, and social phenomena. The project, called “Robust, Interpretable, and Efficient Unsupervised Learning with K-set Clustering,” is expected to have broad applicability in data science.

Modern machine learning techniques aim to design models and algorithms that allow computers to learn efficiently from vast amounts of previously unexplored data, says Balzano. Typically the data is broken down in one of two ways. Dimensionality-reduction uses an algorithm to break down high-dimensional data into low-dimensional structure that is most relevant to the problem being solved. Clustering, on the other hand, attempts to group pieces of data into meaningful clusters of information.

However, explains Balzano, “as increasingly higher-dimensional data are collected about progressively more elaborate physical, biological, and social phenomena, algorithms that aim at both dimensionality reduction and clustering are often highly applicable, yet hard to find.”

Balzano plans to develop techniques that combine the two key approaches used in machine learning to decipher data, while being applicable to data that is considered “messy.” Messy data is data that has missing elements, may be somewhat corrupted, or is filled heterogeneous information – in other words, it describes most data sets in today’s world.

Balzano is an affiliated faculty member of both the Michigan Institute for Data Science (MIDAS) and the Michigan Institute for Computational Discovery and Engineering (MICDE). She is part of a MIDAS-supported research team working on single-cell genomic data analysis.

Read more about the NSF CAREER award…

MIDAS adds Associate Directors to boost campus engagement

By | General Interest, Happenings, News

The Michigan Institute for Data Science (MIDAS) has added two Associate Directors who will help increase outreach to all academic units at the University of Michigan.

  • Pamela Davis-Kean, Professor of Psychology and Research Professor at the Institute for Social Research, will be the new MIDAS Associate Director for Humanities and Social Sciences.
  • Kayvan Najarian, Professor of Computational Medicine and Bioinformatics and Emergency Medicine, will be the new MIDAS Associate Director for Health Sciences.
  • Ivo Dinov, Professor of Health Behavior and Biological Science, will continue as the MIDAS Associate Director for Education and Training.
  • H.V. Jagadish, Professor of Electrical Engineering and Computer Science, and the recently appointed Director of MIDAS, will lead outreach efforts for Engineering and the Physical Sciences.

“The goal is for each associate director to engage with corresponding parts of the University,” said Prof. Jagadish. “At times, that will mean simply being a primary point of contact for researchers engaged in data-driven science. But it will also entail developing data science activities or programs of particular interest to researchers in their respective parts of campus.”

Davis-Kean and Najarian will take their positions on March 1, 2019.

MIDAS was established in 2015 as part of the university-wide Data Science Initiative to promote interdisciplinary collaboration in data science and education. The institute has built a cohort of more than 200 affiliated faculty members who span all three U-M campuses. Institute funding has catalyzed several multidisciplinary research projects, many of which have generated significant external funding. MIDAS also plays a key role in establishing new educational opportunities, such as the graduate certificate in data science, and provides additional support for student groups, including one team that used data science to help address the Flint water crisis.

H.V. Jagadish appointed director of MIDAS

By | General Interest, Happenings, News

H.V. Jagadish has been appointed director of the Michigan Institute for Data Science (MIDAS), effective February 15, 2019.

Jagadish, the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan, was one of the initiators of an earlier concept of a data science initiative on campus. With support from all academic units and the Institute for Social Research, the Office of the Provost and Office of the Vice President for Research, MIDAS was established in 2015 as part of the university-wide Data Science Initiative to promote interdisciplinary collaboration in data science and education.

“I have a longstanding passion for data science, and I understand its importance in addressing a variety of important societal issues,” Jagadish said. “As the focal point for data science research at Michigan, I am thrilled to help lead MIDAS into its next stage and further expand our data science efforts across disciplines.”

Jagadish replaces MIDAS co-directors Brian Athey and Alfred Hero, who completed their leadership appointments in December 2018.

“Professor Jagadish is a leader in the field of data science, and over the past two decades, he has exhibited national and international leadership in this area,” said S. Jack Hu, U-M vice president for research. “His leadership will help continue the advancement of data science methodologies and the application of data science in research in all disciplines.”

MIDAS has built a cohort of 26 active core faculty members and more than 200 affiliated faculty members who span all three U-M campuses. Institute funding has catalyzed several multidisciplinary research projects in health, transportation, learning analytics, social sciences and the arts, many of which have generated significant external funding. MIDAS also plays a key role in establishing new educational opportunities, such as the graduate certificate in data science, and provides additional support for student groups, including one team that used data science to help address the Flint water crisis.

As director, Jagadish aims to expand the institute’s research focus and strengthen its partnerships with industry.

“The number of academic fields taking advantage of data science techniques and tools has been growing dramatically,” Jagadish said. “Over the next several years, MIDAS will continue to leverage the university’s strengths in data science methodologies to advance research in a wide array of fields, including the humanities and social sciences.”

Jagadish joined U-M in 1999. He previously led the Database Research Department at AT&T Labs.

His research, which focuses on information management, has resulted in more than 200 journal articles and 37 patents. Jagadish is a fellow of the Association for Computing Machinery and the American Association for the Advancement of Science, and he served nine years on the Computing Research Association board.

Most CSCAR workshops will be free for the U-M community starting in January 2019

By | Educational, General Interest, Happenings, News

Beginning in January of 2019, most of CSCAR’s workshops will be offered free of charge to UM students, faculty, and staff.

CSCAR is able to do this thanks to funding from UM’s Data Science Initiative.  Registration for CSCAR workshops is still required, and seats are limited.

CSCAR requests that participants please cancel their registration if they decide not to attend a workshop for which they have previously registered.

Note that a small number of workshops hosted by CSCAR but taught by non-CSCAR personnel will continue to have a fee, and fees will continue to apply for people who are not UM students, faculty or staff.

Eric Michielssen completes term as Associate Vice President for Research – Advanced Research Computing

By | General Interest, Happenings, News

Eric Michielssen will step down from his position as Associate Vice President for Research – Advanced Research Computing on December 31, 2018, after serving in that leadership role for almost six years. Dr. Michielssen will return to his faculty role in the Department of Electrical Engineering and Computer Science in the College of Engineering.

Under his leadership, Advanced Research Computing has helped empower computational discovery through the Michigan Institute for Computational Discovery and Engineering (MICDE), the Michigan Institute for Data Science (MIDAS), Advanced Research Computing-Technology Services (ARC-TS) and Consulting for Statistics, Computing and Analytics Research (CSCAR).

In 2015, Eric helped launch the university’s $100 million Data Science initiative, which enhances opportunities for researchers across campus to tap into the enormous potential of big data. He also serves as co-director of the university’s Precision Health initiative, launched last year to harness campus-wide research aimed at finding personalized solutions to improve the health and wellness of individuals and communities.

The Office of Research will convene a group to assess the University’s current and emerging needs in the area of research computing and how best to address them.