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.

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

By | Educational, General Interest, MDSTPosts, MDSTProjects, News | No Comments

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

Undergrad Research Opportunity: Linking Survey and Big Data

By | Educational, General Interest, jobs | No Comments

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.

Ann Arbor Deep Learning annual event — Nov. 12

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

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.

MIDAS co-sponsoring Transportation Research Board’s Transformational Technologies symposium — Oct. 31 & Nov. 1 in Detroit

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

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.

Q & A with Chaoyi Jiao, first recipient of the MIDAS Graduate Certificate in Data Science

By | Educational, General Interest, News | No Comments

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.

For more information on the program, visit http://midas.umich.edu/certificate/ or contact midas-contact@umich.edu.

U-M Professors Jacob Abernethy and Eric Schwartz to speak on “Statistical and Algorithmic Tools to Aid Recovery in Flint” — Sept. 12

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

ABSTRACT: Recovery from the Flint Water Crisis has been hindered by uncertainty in both the water testing process and the causes of contamination. On the other hand, city, state, and federal officials have been collecting and organizing a significant amount of data, including many thousands of water samples, information on pipe materials, and city records. Combining all of this information, and utilizing state-of-the-art algorithmic and statistical tools, we have be able to develop a clearer picture as to the source of the problems, to accurately estimate the greatest risks, and to more efficiently direct resources towards recovery.