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.
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.
a2-dlearn2016 is an annual event bringing together deep learning enthusiasts, researchers and practitioners from a variety of backgrounds.
The event will include speakers from the University of Michigan, University of Toronto, Toyota Research Institute and MDA Information Systems.
With input from MIDAS, four research teams from the University of Michigan and Shanghai Jiao Tong University in China are sharing $800,000 in awards to study depression, electric vehicles, urban green space and bone cancer.
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.
he 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.
This symposium will bring together leaders from the public and private sectors and academia to meet the challenges posed by deployment of transformational transportation technologies. MIDAS affiliated faculty members Carol Flannagan, Al Hero and Pascal Van Hentenryck will be speaking.
For more information, visit the event website.
The Michigan Institute for Data Science (MIDAS) hosted Dr. Gary King of Harvard University for a talk titled “Big Data is Not About the Data!” on Friday, Oct. 3 as part of the MIDAS Seminar Series.
Video of the talk is now available for viewing online.
For a schedule of upcoming MIDAS Seminars, visit the seminar webpage.
The Michigan Data Science Team, with support from MIDAS, won the best poster competition at the Meeting the Challenges of Safe Transportation in an Aging Society Symposium Sept. 14-15, 2016.
The MIDAS Graduate Certificate in Data Science was established in 2015 to offer students a way to enhance their skills and prepare for a workforce that values multidisciplinary knowledge, broad analytical skills, and agile technological abilities. Nearly 50 students have enrolled in the program, which requires 9 credits of courses and 3 credits of experiential training, and involves mentorship opportunities with MIDAS-affiliated faculty members.
Chaoyi Jiao, who recently received his Ph.D from the Department of Climate and Space Sciences and Engineering and is now a post-doc there, was the first recipient of the MIDAS Graduate Certificate in Data Science. He recently answered a few questions about the program.
What are your research interests, and how does “data science,” broadly speaking, pertain to them?
My research primarily focuses on the Arctic climate change and climate modeling. Observation shows that the Arctic is warming at a much more rapid pace compared to the middle latitudes and tropics. Thus further warming of the climate system may pose an increasing threat to the climate and ecosystem in the Arctic. I hope to gain better understanding of the Arctic climate change and improve the numerical representation of Arctic climate in the climate models. As the data generated by the current generation of climate models and observational networks are growing rapidly, more sophisticated data analyses skills become more and more important for this research area.
Why did you decide to pursue the Graduate Certificate in Data Science?
As I started to conduct my PhD research project, I realized that statistics and data analysis skills are quite important. So I started to take some statistics classes on my second year. Later I learnt that there is a Data Science certificate program. I was very interested in the learning opportunities and academic experience proposed by this program. And I also think it could greatly benefit my career. So I decided to apply.
How hard or easy was it to meet the academic requirements?
Some classes are quite challenging when I started. But generally speaking, I think the academic requirement of this certificate is quite reasonable.
Were you required to take courses that you wouldn’t otherwise have taken? If so, how did they help you broaden your view of data science?
I would say probably not. I was planning to take some courses relate to statistics and machine learning topics before this certificate becomes available. But I think if I enrolled the data science program at a earlier time, I may take one or two extra classes. My experience tells that taking classes outside one’s own research field often helps to think with a broader perspective.
Why should other U-M students pursue this certificate?
I think as many research fields are becoming more and more data driven, mastering the cutting edge data analysis skills can greatly benefit one’s career. I would say if you believe that your research field is data driven and you hope to learn more advanced data science related topics, you definitely should consider this certificate. Moreover, the data science certificate also provides a great opportunities for networking with other students in this program.
The new Data Acquisition for Data Science (DADS) program supports acquisition, preparation, management, and maintenance of specialized research data sets used in current and future data science-enabled research projects across U-M, with special focus on the four challenge initiative areas pursued by the Michigan Institute for Data Science (MIDAS): transportation science, health science, social science, and learning analytics.
DADS is meant to provide datasets that can be used by multiple U-M researchers and departments.
DADS is funded through the Data Science Initiative (DSI); total funding is capped at $200,000 per year for 5 years.
DADS will be managed jointly by the Library and Advanced Research Computing (ARC), with support from ARC’s Consulting for Statistics, Computing, and Analytics Research (CSCAR), MIDAS, and ARC-Technology Services (ARC-TS) units.
For more information, see arc.umich.edu/dads.