Data science institutes at University of Michigan and University College London sign academic cooperation agreement

By | Al Hero, Educational, General Interest, News | No Comments
From left, Al Hero, U-M; Patrick Wolfe, UCL; and Brian Athey, U-M signed an agreement for research and educational cooperation between the University of Michigan and University College London.

From left, Al Hero, U-M; Patrick Wolfe, UCL; and Brian Athey, U-M signed an agreement for research and educational cooperation between the University of Michigan and University College London.

ANN ARBOR, MI and LONDON — The Michigan Institute of Data Science (MIDAS) at the University of Michigan and the Centre for Data Science and Big Data Institute at UCL (University College London) have signed a five-year agreement of scientific and academic cooperation.

The agreement sets the stage for collaborative research projects between faculty of both institutions; student exchange opportunities; and visiting scholar arrangements, among other potential partnerships.

“There is a lot of common ground in what we do,” said Patrick Wolfe, Executive Director of UCL’s Centre for Data Science and Big Data Institute. “Both MIDAS and UCL cover the full spectrum of data science domains, from smart cities to healthcare to transportation to financial services, and both promote cross-cutting collaboration between scientific disciplines.”

Alfred Hero, co-director of MIDAS and professor of Electrical Engineering and Computer Science at U-M, said that one of the original goals of the institute when it was founded in 2015 under U-M’s $100 million Data Science Initiative was to reach out to U.S. and international partners.

“It seemed very natural that this would be the next step,” Hero said, adding that it would complement MIDAS’s recent partnership with the Shenzhen Research Institute of Big Data in China. “UCL epitomizes the collaboration, multi-disciplinarity and multi-institutional involvement that we’re trying to establish in our international partnerships.”

Wolfe visited Ann Arbor in early January to sign a memorandum of understanding along with Hero and Brian Athey, professor of bioinformatics and the other MIDAS co-director.

The agreement lists several potential areas of cooperation, including:

  • joint research projects
  • exchange of academic publications and reports
  • sharing of teaching methods and course design
  • joint symposia, workshops and conferences
  • faculty development and exchange
  • student exchange
  • exchange of visiting research scholars.

Links:

MIDAS at U-M

UCL Big Data Institute

Follow UCL’s data science activities @uclbdi

Follow MIDAS at @ARC_UM

MIDAS Co-Director Al Hero receives 2016-2017 Stephen S. Attwood Award

By | Al Hero, General Interest, News | No Comments

Al Hero, Co-Director for the Michigan Institute for Data Science (MIDAS), has received the 2016-2017 Stephen S. Attwood Award, the highest honor awarded to a faculty member by the College of Engineering for “extraordinary achievement in teaching, research, service, and other activities that have brought distinction to the College and University.”  More information on this prestigious honor are at http://eecs.umich.edu/eecs/about/articles/2017/al-hero-receives-coe-stephen-attwood-award.html.

 

MIDAS announces second round of Data Science Challenge Initiative awards, in health and social science

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Five research projects — three in health and two in social science — have been awarded funding in the second round of the Michigan Institute for Data Science Challenge Initiative program.

The projects will receive funding from MIDAS as part of the Data Science Initiative announced in fall 2015.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science. For more information, visit midas.umich.edu/challenges.

The projects, determined by a competitive submission process, are:

  • Title: Michigan Center for Single-Cell Genomic Data Analysis
    Description: The center will establish methodologies to analyze sparse data collected from single-cell genome sequencing technologies. The center will bring together experts in mathematics, statistics and computer science with biomedical researchers.
    Lead researchers: Jun Li, Department of Human Genetics; Anna Gilbert, Mathematics
    Research team: Laura Balzano, Electrical Engineering and Computer Science; Justin Colacino, Environmental Health Sciences; Johann Gagnon-Bartsch, Statistics; Yuanfang Guan, Computational Medicine and Bioinformatics; Sue Hammoud, Human Genetics; Gil Omenn, Computational Medicine and Bioinformatics; Clay Scott, Electrical Engineering and Computer Science; Roman Vershynin, Mathematics; Max Wicha, Oncology.
  • Title: From Big Data to Vital Insights: Michigan Center for Health Analytics and Medical Prediction (M-CHAMP)
    Description: The center will house a multidisciplinary team that will confront a core methodological problem that currently limits health research — exploiting temporal patterns in longitudinal data for novel discovery and prediction.
    Lead researchers: Brahmajee Nallamothu, Internal Medicine; Ji Zhu, Statistics; Jenna Wiens, Electrical Engineering and Computer Science; Marcelline Harris, Nursing.
    Research team: T. Jack Iwashyna, Internal Medicine; Jeffrey McCullough, Health Management and Policy (SPH); Kayvan Najarian, Computational Medicine and Bioinformatics; Hallie Prescott, Internal Medicine; Andrew Ryan, Health Management and Policy (SPH); Michael Sjoding, Internal Medicine; Karandeep Singh, Learning Health Sciences (Medical School); Kerby Shedden, Statistics; Jeremy Sussman, Internal Medicine; Vinod Vydiswaran, Learning Health Sciences (Medical School); Akbar Waljee, Internal Medicine.
  • Title: Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology
    Description: Using an app platform that integrates signals from both mobile phones and wearable sensors, the project will collect data from over 1,000 medical interns to identify the dynamic relationships between mood, sleep and circadian rhythms. These relationships will be utilized to inform the type and timing of personalized data feedback for a mobile micro-randomized intervention trial for depression under stress.
  • Lead researchers: Srijan Sen, Psychiatry; Margit Burmeister, Molecular and Behavioral Neuroscience.
    Research team:  Lawrence An, Internal Medicine; Amy Cochran, Mathematics; Elena Frank, Molecular and Behavioral Neuroscience; Daniel Forger, Mathematics; Thomas Insel (Verily Life Sciences); Susan Murphy, Statistics; Maureen Walton, Psychiatry; Zhou Zhao, Molecular and Behavioral Neuroscience.
  • Title: Computational Approaches for the Construction of Novel Macroeconomic Data
    Description: This project will develop an economic dataset construction system that takes as input economic expertise as well as social media data; will deploy a data construction service that hosts this construction tool; and will use this tool and service to build an “economic datapedia,” a compendium of user-curated economic datasets that are collectively published online.
    Lead researcher: Matthew Shapiro, Department of Economics
    Research team: Michael Cafarella, Computer Science and Engineering; Jia Deng, Electrical Engineering and Computer Science; Margaret Levenstein, Inter-university Consortium for Political and Social Research.
  • Title: A Social Science Collaboration for Research on Communication and Learning based upon Big Data
    Description: This project is a multidisciplinary collaboration meant to introduce social scientists, computer scientists and statisticians to the methods and theories of engaging observational data and the results of structured data collections in two pilot projects in the area of political communication and one investigating parenting issues. The projects involve the integration of geospatial, social media and longitudinal data.
    Lead researchers: Michael Traugott, Center for Political Studies, ISR; Trivellore Raghunathan, Biostatistics
    Research team: Leticia Bode, Communications, Georgetown University; Ceren Budak, U-M School of Information; Pamela Davis-Keane, U-M Psychology, ISR; Jonathan Ladd, Public Policy, Georgetown; Zeina Mneimneh, U-M Survey Research Center; Josh Pasek, U-M Communications; Rebecca Ryan, Public Policy, Georgetown; Lisa Singh, Public Policy, Georgetown; Stuart Soroka, U-M Communications.

For more details, see the press releases on the social science and health science projects.

MIDAS to host faculty meeting on NSF BIGDATA solicitation

By | Funding Opportunities, General Interest, News | No Comments

The Michigan Institute for Data Science (MIDAS) will hold a faculty meeting at noon on Thursday, January 19 (Suite 7625, School of Public Health I, 1415 Washington Heights) for the NSF 17-534 “Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)” solicitation.

The meeting will include an overview of the NSF solicitation, U-M Data Science Resources (MIDAS, CSCAR, ARC-TS) available to faculty responding to the NSF call, and an opportunity to network with other faculty.

MIDAS has also arranged for Sylvia Spengler, NSF CISE Program Director, to be available at 1:30 pm to answer questions regarding the BIGDATA solicitation.

We invite you to participate in the faculty meeting to share your ideas and interest in responding to this BIGDATA solicitation as well as interact with other faculty looking to respond to this funding mechanism.

For those unable to participate in person, you can join virtually using GoToMeeting:

A box lunch will be provided at the faculty meeting.  Your RSVP (https://goo.gl/forms/OYAuB8mWCOlx3fw73) is appreciated.

Women in Data Science Conference — Feb. 3, Michigan League

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In partnership with Stanford University, MIDAS will participate in the 2017 Women in Data Science Conference, with live speakers on campus and a simulcast of the conference proceedings from Stanford.

Speakers at the U-M event include Amy Cohn (COE), Stephanie Teasley (SI), Yi Lu Murphey (ECE-Dearborn), and Yao Xie (Georgia Institute of Technology).

For more information, including registration, visit the U-M WIDS page.

Undergrad Research Opportunity: Linking Survey and Big Data

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Linking existing social survey data to administrative (big) data sources is a powerful way to expand the data available for sociological inquiry. This project pursues a range of different linkage projects. We will add historical Census data as well as rich data on housing from a real estate vendor to ongoing, large-scale survey studies of American families. These matched data will end up supporting exciting new opportunities for research on the long-term trends in economic wellbeing and the transmission of social inequality across generations in the United States.

U-M professors and students develop app to help Flint residents identify lead risks

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A mobile app and website built for the city of Flint is available now to help the community and government agencies manage the ongoing water crisis.

Mywater-Flint, for Android and online at Mywater-flint.com, was developed by computer science researchers at the University of Michigan’s Flint and Ann Arbor campuses and funded by Google.org. Through it, residents and city employees can:

  • Access a citywide map of where lead has been found in drinking water.
  • Discover where service line workers have replaced infrastructure that connects. homes to the water main, and where they’re currently working.
  • Locate the nearest distribution centers for water and water filters.
  • Find step-by-step instructions for water testing.
  • Determine the likelihood that the water in a home or another location is contaminated, among other features.

Ann Arbor Deep Learning annual event — Nov. 12

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a2-dlearn2016 is an annual event bringing together deep learning enthusiasts, researchers and practitioners from a variety of backgrounds.

MIDAS is proud to co-sponsor the event, which began last year as a collaboration between the Ann Arbor – Natural Language Processing and Machine Learning: Data, Science and Industry meetup groups.

The event will include speakers from the University of Michigan, University of Toronto, Toyota Research Institute and MDA Information Systems.

Please visit the event website for more information. Registration is required as space is limited.

Research highlights: U-M group awarded Midwest Big Data Spoke award from NSF for Advanced Computational Neuroscience Network (ACNN)

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A group of three University of Michigan faculty members will lead the Advanced Computational Neuroscience Network project as a “spoke” in the Midwest Big Data Hub program funded by the National Science Foundation.

The Principal Investigator is Richard Gonzalez, Amos N. Tversky Collegiate Professor of the U-M Psychology Department, who has joint appointments in Statistics and Marketing, is Director of the Research Center for Group Dynamics, Research Professor in the Center for Human Growth and Development, and has affiliations with the U-M Comprehensive Cancer Center and the Center for Computational Medicine and Bioinformatics.

Co-PI’s are George Alter, professor in the History Department and the Institute for Social Research, and Ivo Dinov, associate professor in the School of Nursing and School of Medicine and Director of Statistics Online Computational Resources (SOCR), and associate director for Education and Training of the Michigan Institute for Data Science (MIDAS).

All three are affiliated faculty of MIDAS.

The ACNN program will leverage rapid technological development in sensing, imaging, and data analysis to facilitate new discoveries in neuroscience, and will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral sciences together with partnerships in academia, industry and government. ACNN will address three specific problems relating to Big Data in neuroscience:

  • data capture, organization and management involving multiple centers and research groups
  • quality assurance, preprocessing and analysis that incorporates contextual metadata
  • data communication to software and hardware computational resources that can scale with the volume, velocity and variety of neuroscience data sets.

ACNN is a collaboration between U-M, Ohio State University, Indiana University, and Case Western Reserve University.

The BD Hubs and Spokes programs are part of a larger effort at NSF to advance data science and engineering. In Fiscal Year 2017, NSF will invest more than $110 million in Big Data research.