NSF Learning Analytics Workshop

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This NSF workshop brings together learning and data scientists with various backgrounds and prior expertise to collaboratively solve the research challenges of development of instruction, assessment of competence of current and would-be workers, and evaluation of learning tools.  Three specific questions will be addressed: how to define competence, measure competence and evaluate new approaches to learning.  Speakers are invited from across industry and academia to ensure a broad perspective and specifically to take into account employer’s perspectives.  Please join us for an exciting event and lively discussions.

Please register if you would like to attend.

March 18, 2019
8:00 a.m. – Registration
8:30 a.m.  – Welcome and Introductions

  • Stephanie Teasley, Research Professor, School of Information, University of Michigan
  • Rada Mihalcea, Professor, Computer Science and Engineering, University of Michigan
  • Henry Kelly, Senior Scientist, Michigan Institute for Data Science, University of Michigan

8:45 a.m. – Talks and discussion on defining competence

Marie Cini

President and CEO

The Council for Adult and Experiential Learning



David Blake





9:40 a.m. – Talks and discussion on measuring competence

Bror Saxberg

Vice President

Learning Science at Chan Zuckerberg Initiative



Tammy Wang

Vice President

Data Science and Analytics at Riviera Partners



10:35 a.m. – Talks and discussion on evaluating new approaches to learning

Norman Bier


Open Learning Initiative, DataLab, Carnegie Mellon University



Yun Jin Rho


Efficacy Analytics and Studies, Pearson



11:30 a.m. to 12:00 p.m. – Networking

March 19, 2019

9:45 a.m. to 12:50 p.m. –  Panel discussions on each of the three topics
12:50 p.m. – Concluding remarks and discussion of next steps
1:00 p.m. – Adjourn

MIDAS Working Group on NSF solicitation for Big Data Spokes

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This working group is organized by MIDAS to respond to the NSF Big Data Spokes funding solicitation (BD Spokes).  


  • A brief presentation about the funding opportunity and the background of the regional BD Hubs (Midwest, Northeast, South and West) and existing Spokes
  • Discussion of ideas of new Spokes and forming collaborative teams (within and outside of UM)
  • Discussion on aligning and working with regional BD hubs

The BD Spokes will work with regional BD hubs and existing spokes to accelerate progress towards societal grand challenges in regional and national priority areas, help automate the Big Data lifecycle, enable access to and increase the use of important data sets, and contribute to education and training.  The focus areas specified by NSF include: Education; Data Intensive Research in the Social, Behavioral, and Economic Sciences; Data-driven Research in Chemistry; Neuroscience; Data Analytics for Security; Replicability and Reproducibility in Data Science.  All researchers interested in proposing or collaborating on new BD Spokes are welcome to our working group.

Important Dates:

  • May 17, 2017: University of Michigan internal deadline for declaring intent.  NSF allows only one proposal from each university as the lead institution
  • Sept. 18, 2017: full proposal due to NSF

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

NSF Federal Datasets Faculty Working Group

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In response to a recent NSF solicitation (Dear Colleague Letter: Request for Input on Federal Datasets with Potential to Advance Data Science), the Michigan Institute for Data Science (MIDAS) invites Faculty to join a faculty working group to collaborate on a joint submission (deadline: March 31).

The NSF DCL working group will identify federal government data that will enhance and support the growing data science research community. We are being asked what federal data is of value for data science and machine learning that will have significant impact on science, engineering, education, and society.

If you have experience or interest in using federal datasets for your research, and would like to help shape how federal datasets can be preserved and utilized, please join this working group. We will discuss strategies for responding to NSF and potential funding (both federal and local) to support this effort. Please attend in person if possible.


MIDAS to host faculty meeting on NSF BIGDATA solicitation

By | Funding Opportunities, General Interest, News

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.

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

By | General Interest, Happenings, News, Research

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.