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.
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.
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.
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.
Students interested in computational and data science are invited to learn about graduate programs that will prepare them for success in computationally intensive fields. Pizza and pop will be provided.
Two sessions are scheduled:
Monday, Sept. 19, 5 – 6 p.m.
Johnson Rooms, Lurie Engineering Center (North Campus)
Wednesday, Sept. 21, 5 – 6 p.m.
2001 LSA Building (Central Campus)
The sessions will address:
- The Ph.D. in Scientific Computing, which is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
- The Graduate Certificate in Computational Discovery and Engineering, which trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
- The Graduate Certificate in Data Science, which is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.
This summer, 10 high school students from around the country gathered in Ann Arbor for the first annual Michigan Institute for Data Science Summer Camp on the campus of the University of Michigan.
The weeklong camp, titled “From Simple Building Blocks to Complex Shapes: A Visual Tour of Fourier Series,” drew students from as far away as Kansas City, MO, and as nearby as Ypsilanti and Ann Arbor.
The camp was organized by Raj Nadakuditi, assistant professor in the Electrical Engineering and Computer Science Department. Other U-M faculty instructors at the camp were Prof. Jenna Weins, and MIDAS co-directors Prof. Al Hero and Prof. Brian Athey.
The camp was well received by the participants, who ranged from high school sophomores to seniors. A total of 10 students attended, five boys and five girls. Students used the Fourier Series to make art, diagnose disease, and “play detective.”
“I’ve been looking to learn about what been going on with Big Data,” said Daniel Neamati, a 16-year-old from Ann Arbor who hopes to someday study deep space with NASA. “I was really surprised by this camp. Math is basically everywhere.”
Elizabeth Fitzgerald, 16, traveled from South Carolina to take part in the camp. She said she wants to study artificial intelligence and machine learning, but was interested to see what else data science can explain.
“It was enlightening to see all the different applications of data science,” she said.
The camp will be offered annual. Details for next year will be posted at http://midas.umich.edu/camp/ in the coming months.
A group of U-M students has won second place in the National Institute on Drug Abuse (NIDA) “Addiction Research: There’s an App for That” challenge.
The project was called “Substance Abuse Research Assistant (SARA).” Tthe team was composed of undergraduates (Steven Zeng and Joshua Song from Computer Science, and Amy Afonso and Wan-Ting Lin from the School of Information) and led by a masters student (Andy Lee, SI). The faculty mentors were Pedja Klasnja, Susan Murphy, Ambuj Tewari,and Maureen Walton. Support was provided by the Michigan Institute for Data Science (MIDAS).
The second place award carries a cash prize of $25,000.
See the NIDA Challenges website for more information on challenges.