The dates of the camp are all day May 14th-18th.
University of Michigan students won first and third places in the Michigan Datathon held Nov. 4, 2017 in the Michigan Union and hosted by Citadel LLC, Correlation One, and the U-M Statistics Department.
1st-place winning team from the University of Michigan:
Ruofei (Brad) Zhao, Statistics Ph.D. student
Zheng Gao, Statistics Ph.D. student
You Wu, Master’s in Applied Statistics student
Kevin Zheng, Sophomore, Computer Science
Zi Yi, Statistics Master’s student, University of Chicago
Tian Gu, Biostatistics Ph.D. student, University of Michigan
Shuo Zhang, Statistics Master’s student, University of Chicago
Shiyang Lu, Robotics & Naval Architecture and Marine Engineering Master’s student, University of Michigan
3rd-place team from the University of Michigan:
Hanbo Sun, Master’s in Applied Statistics student
Xinghui Song, Master’s in Applied Statistics student
Tuo Wang, Master’s in Applied Statistics student
Hang Yuan, Master’s in Applied Statistics student
In this competition, student teams at the University of Michigan will use historical free agent data to predict the value of new contracts signed in the 2018 free agency period. These predictions will be evaluated against the actual contracts as they are signed. This competition is organized by the Michigan Data Science Team (MDST), in collaboration with the Baltimore Ravens and the Michigan Sports Analytics Society (MSAS). Food will be provided. This is an initial kick-off meeting of the competition.
500 Church St, 48104, MI
CSCAR now provides walk-in support during business hours for students, faculty, and staff seeking assistance in getting started with the Flux computing environment. CSCAR consultants can walk a researcher through the steps of applying for a Flux account, installing and configuring a terminal client, connecting to Flux, basic SSH and Unix command line, and obtaining or accessing allocations.
In addition to walk-in support, CSCAR has several staff consultants with expertise in advanced and high performance computing who can work with clients on a variety of topics such as installing, optimizing, and profiling code.
Support via email is also provided via email@example.com.
CSCAR is located in room 3550 of the Rackham Building (915 E. Washington St.). Walk-in hours are from 9 a.m. – 5 p.m., Monday through Friday, except for noon – 1 p.m. on Tuesdays.
See the CSCAR web site (cscar.research.umich.edu) for more information.
Advanced Research Computing at U-M (ARC) will host an information session for graduate students in all disciplines who are interested in new computing and data science resources and services available to U-M researchers.
Brief presentations from members of ARC Technology Services (ARC-TS) on computing infrastructure, and from Consulting for Statistics, Computing, and Analytics Research (CSCAR) on statistics, data science, and computing training and consulting will be followed by a Q&A session, and opportunities to interact individually with ARC and CSCAR staff.
ARC and CSCAR are interested in connecting with graduate students whose research would benefit from customized or innovative computational or analytic approaches, and can provide guidance for students aiming to do this. ARC and CSCAR are also interested in developing training and documentation materials for a diverse range of application areas, and would welcome input from student researchers on opportunities to tailor our training offerings to new areas.
- Kerby Shedden, Director, CSCAR
- Brock Palen, Director, ARC-TS
Wednesday, Nov. 8, 2017, 2 – 4 p.m., West Conference Room, 4th Floor, Rackham Building (915 E. Washington St.)
More than 50 students took part in the Student Poster Competition during the 3rd Annual MIDAS Symposium.
The winning entries can be viewed at http://midas.umich.edu/2017-symposium/winning-posters/
The University of Michigan Library system and the Data Acquisition for Data Sciences program (DADS) of the U-M Data Science Initiative (DSI) have recently joined forces to license a major data resource capturing parcel-level information about the property market in the United States.
The data were licensed from the Corelogic corporation, who have assimilated deed, tax and foreclosure information on nearly all properties in the entire US. Coverage dates vary by county, some county records go back fifty years. Coverage is more comprehensive from the 1990s to the present.
These data will support a variety of research efforts into regional economies, economic disparities, trends in land-use, housing market dynamics, and urban ecology, among many other areas.
The data are available on the Turbo Research Storage system for users of the U-M High Performance Computing infrastructure, and via the University of Michigan Library.
To access the data, researchers must first sign a MOU; contact Senior Associate Librarian Catherine Morse firstname.lastname@example.org for more information, or visit https://www.lib.umich.edu/database/corelogic-parcel-level-real-estate-data.
Asst. Prof. Emanuel Gull, Physics, is offering a mini-course introducing the Python programming language in a four-lecture series. Beginners without any programming experience as well as programmers who usually use other languages (C, C++, Fortran, Java, …) are encouraged to come; no prior knowledge of programming languages is required!
For the first two lectures we will mostly follow the book Learning Python. This book is available at our library. An earlier edition (with small differences, equivalent for all practical purposes) is available as an e-book. The second week will introduce some useful python libraries: numpy, scipy, matplotlib.
At the end of the first two weeks you will know enough about Python to use it for your grad class homework and your research.
Special meeting place: we will meet in 340 West Hall on Monday September 11 at 5 PM.
Please bring a laptop computer along to follow the exercises!
Syllabus (Dates & Location for Fall 2017)
- Monday September 11 5:00 – 6:30 PM: Welcome & Getting Started (hello.py). Location: 340 West Hall
- Tuesday September 12 5:00 – 6:30 PM: Numbers, Strings, Lists, Dictionaries, Tuples, Functions, Modules, Control flow. Location: 335 West Hall
- Wednesday September 13 5:00 – 6:30 PM: Useful Python libraries (part I): numpy, scipy, matplotlib. Location: 335 West Hall
- Thursday September 14 5:00 – 6:30 PM: Useful Python libraries (part 2): 3d plotting in matplotlib and exercises. Location: 335 West Hall
Five research teams from the University of Michigan and Shanghai Jiao Tong University in China are sharing $1 million to study data science and its impact on air quality, galaxy clusters, lightweight metals, financial trading and renewable energy.
Since 2009, the two universities have collaborated on a number of research projects that address challenges and opportunities in energy, biomedicine, nanotechnology and data science.
In the latest round of annual grants, the winning projects focus on data science and how it can be applied to chemistry and physics of the universe, as well as finance and economics.
For more, read the University Record article.
For descriptions of the research projects, see the MIDAS/SJTU partnership page.
The Amazon Research Awards (ARA) program offers awards of up to $80,000 in cash and $20,000 in AWS promotional credits to faculty members at academic institutions in North America and Europe for research in these areas:
- Computer vision
- General AI
- Knowledge management and data quality
- Machine learning
- Machine translation
- Natural language understanding
- Search and information retrieval
- Security, privacy and abuse prevention
The ARA program funds projects conducted primarily by PhD students or post docs, under the supervision of the faculty member awarded the funds. To encourage collaboration and the sharing of insights, each funded proposal team is assigned an appropriate Amazon research contact. Amazon invites ARA recipients to speak at Amazon offices worldwide about their work, meet with Amazon research groups face-to-face, and encourages ARA recipients to publish their research outcome and commit related code to open-source code repositories.
Submissions are to be made online and details including rules and who may apply are located here.