New private insurance claims dataset and analytic support now available to health care researchers

By | General Interest, Happenings, HPC, News | No Comments

The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.

The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.  

To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.

CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.

Contact Patrick Brady (pgbrady@umich.edu) at IHPI or James Henderson (jbhender@umich.edu) at CSCAR for more information.

The data acquisition and availability was funded by IHPI and the U-M Data Science Initiative.

MIDAS starting research group on mobile sensor analytics

By | Educational, Events, General Interest, Happenings, News | No Comments

The Michigan Institute for Data Science (MIDAS) is convening a research working group on mobile sensor analytics. Mobile sensors are taking on an increasing presence in our lives. Wearable devices allow for physiological and cognitive monitoring, and behavior modeling for health maintenance, exercise, sports, and entertainment. Sensors in vehicles measure vehicle kinematics, record driver behavior, and increase perimeter awareness. Mobile sensors are becoming essential in areas such as environmental monitoring and epidemiological tracking.

There are significant data science opportunities for theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.

Our working group welcomes researchers with interest in mobile sensor analytics in any scientific domain, including but not limited to health, transportation, smart cities, ecology and the environment.

Where and When:

Noon to 2 pm, April 13, 2017

School of Public Health I, Room 7625

Lunch provided

Agenda:

  • Brief presentations about challenges and opportunities in mobile sensor analytics (theory and application);

  • A brief presentation of a list of funding opportunities;

  • Discussion of research ideas and collaboration in the context of grant application and industry partnership.

Future Plans: Based on the interest of participants, MIDAS will alert researchers to relevant funding opportunities, hold follow-up meetings for continued discussion and team formation as ideas crystalize for grant applications, and work with the UM Business Engagement Center to bring in industry partnership.

Please RSVP.  For questions, please contact Jing Liu, Ph.D, MIDAS research specialist (ljing@umich.edu; 734-764-2750).

Gilbert, Rudelson, Wu named Simons Foundation Fellows in Mathematics

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Three University of Michigan professors have been named Simons Fellows in Mathematics by the Simons Foundation:

  • Anna Gilbert, the Herman H. Goldstine Collegiate Professor of Mathematics, core faculty member at the Michigan Institute for Data Science (MIDAS), and Professor of Electrical and Computer Engineering
  • Mark Rudelson, Professor of Mathematics
  • Sijue Wu, Robert W. and Lynn H. Browne Professor of Science and Professor of Mathematics.

Forty fellows were named in all.

The fellowships provide funding that allows faculty to take up to a semester-long research leave from teaching and administrative duties. The foundation also gives fellowships in Theoretical Physics.

For more information, see www.simonsfoundation.org.

Workshop co-chaired by MIDAS co-director Prof. Hero releases proceedings on inference in big data

By | Al Hero, Educational, General Interest, Research | No Comments

The National Academies Committee on Applied and Theoretical Statistics has released proceedings from its June 2016 workshop titled “Refining the Concept of Scientific Inference When Working with Big Data,” co-chaired by Alfred Hero, MIDAS co-director and the John H Holland Distinguished University Professor of Electrical Engineering and Computer Science.

The report can be downloaded from the National Academies website.

The workshop explored four key issues in scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models.

The workshop brought together statisticians, data scientists and domain researchers from different biomedical disciplines in order to identify new methodological developments that hold significant promise, and to highlight potential research areas for the future. It was partially funded by the National Institutes of Health Big Data to Knowledge Program, and the National Science Foundation Division of Mathematical Sciences.

Emily Mower Provost Receives NSF CAREER Award to Develop Emotion and Mood Recognition for Mental Health Monitoring and Treatment

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Prof. Emily Mower Provost has been awarded an NSF CAREER grant for her research project, “Automatic Speech-Based Longitudinal Emotion and Mood Recognition for Mental Health Monitoring and Treatment.”

Prof. Mower Provost’s research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology. The goals of her research are motivated by the complexities of human emotion expression and perception.

More information about the project is available from the College of Engineering and Prof. Mower Provost’s CAREER Award Posting by NSF.

Ambuj Tewari selected as 2017 Alfred P. Sloan Research Fellow in Computer Science

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Ambuj Tewari is one of seven U-M scientists selected for Sloan Fellowships.  Dr. Tewari obtained his PhD from UC Berkeley, and joined the University of Michigan as Assistant Professor of Statistics in 2012.  He also holds a courtesy appointment at the Department of Electrical Engineering and Computer Science.  Dr. Tewari is also an Affiliate Faculty member in MIDAS.

The two-year, $60,000 fellowships are awarded to scientists “in recognition of distinguished performance and a unique potential to make substantial contributions to their field,” according to the organization’s website. Researchers are considered based on nominations, and then selected by an independent panel of senior scientists.

The Alfred P. Sloan Foundation supports early career researchers in eight fields, including chemistry, computational and evolutionary molecular biology, computer science, economics, mathematics, neuroscience, ocean sciences, and physics.

For more information, please see the U-M news release.

MDST announces Detroit blight data challenge; organizational meeting Feb. 16

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The Michigan Data Science Team and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit on a data challenge that seeks to answer the question: How can blight ticket compliance be increased?

An organizational meeting is scheduled for Thursday, Feb. 16 at 5:30 p.m. in EECS 1200.

The city is making datasets available containing building permits, trades permits, citizens complaints, and more.

The competition runs through March 15. For more information, see the competition website.

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.