Explore ARCExplore ARC

Interdisciplinary Committee on Organizational Studies (ICOS) Big Data Summer Camp, May 14-18

By | Data, Educational, General Interest, Happenings, News
Social and organizational life are increasingly conducted online through electronic media, from emails to Twitter feed to dating sites to GPS phone tracking. The traces these activities leave behind have acquired the (misleading) title of “big data.” Within a few years, a standard part of graduate training in the social sciences will include a hefty dose of “using of big data,” and we will all be utilizing terms like API and Python.
This year ICOS, MIDAS, and ARC are again offering a one-week “big data summer camp” for doctoral students interested in organizational research, with a combination of detailed examples from researchers; hands-on instruction in Python, SQL, and APIs; and group work to apply these ideas to organizational questions.  Enrollment is free, but students must commit to attending all day for each day of camp, and be willing to work in interdisciplinary groups.

The dates of the camp are all day May 14th-18th.

https://ttc.iss.lsa.umich.edu/ttc/sessions/interdisciplinary-committee-on-organizational-studies-icos-big-data-summer-camp-3/ 

U-M will hold “hackathon” for health communication, with help from Sanjay Gupta and family

By | Educational, General Interest, Happenings, News

Disease outbreaks. Medical discoveries. Natural disasters. The hope — and hype — that can come with new treatment options.

Sanjay Gupta, M.D. has covered them all in his years as medical correspondent for CNN. He’s seen over and over the crucial role of communication in responding to the health effects of every kind of crisis. He’s also seen the delays, missed opportunities and even tragedy that can come from poor communication of health information.

That’s why he and his wife Rebecca have teamed up with his alma mater, the University of Michigan, to support an effort to bring new ideas and tools to health communication.

Application is now open for participation in marathon event March 23-25, focused on innovation for sharing information in times of crisis & beyond.

Read more….

U-M launches Data Science Master’s Program

By | Educational, General Interest, Happenings, News

The University of Michigan’s new, interdisciplinary Data Science Master’s Program is taking applications for its first group of students. The program is aimed at teaching participants how to extract useful knowledge from massive datasets using computational and statistical techniques.

The program is a collaboration between the College of Engineering (EECS), the College of Literature Science and the Arts (Statistics), the School of Public Health (Biostatistics), the School of Information, and the Michigan Institute for Data Science.

“We are very excited to be offering this unique collaborative program, which brings together expertise from four key disciplines at the University in a curriculum that is at the forefront of data science,” said HV Jagadish, Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science, who chairs the program committee for the program.

“MIDAS was a catalyst in bringing  faculty from multiple disciplines together to work towards the development of this new degree program,”  he added.

MIDAS will provide students in this program with interdisciplinary collaborations, intellectual stimulation, exposure to a broad range of practice, networking opportunities, and space on Central Campus to meet for formal and informal gatherings.

For more information, see the program website at https://lsa.umich.edu/stats/masters_students/mastersprograms/data-science-masters-program.html, and the program guide (PDF) at https://lsa.umich.edu/content/dam/stats-assets/StatsPDF/MSDS-Program-Guide.pdf.

Applications are due March 15.

Student data science competition winners visit Quicken Loans headquarters in Detroit

By | Educational, General Interest, MDSTPosts, News

Earlier this year, three Data Science Team (MDST) members — winners of the Quicken Loan (QL) Lending Strategies Prediction Challenge — traveled to Detroit to visit QL headquarters, accept their prizes, and present their findings to the company’s Data Science team.

Back row left to right: Reddy Rachamallu, Alexandr, Alex, Mark Nuppnau, Brian Ball
Front row left to right: Jingshu Chen, Patrick, Alex’s wife Kenzie, Yvette Tian, Mike Tan, and Catherine Tu.

 

Alexander Zaitzeff, a graduate student in the Applied and Interdisciplinary Mathematics program won first place; Alexandr Kalinin, a Bioinformatics graduate student earned second; and Patrick Belancourt, a graduate student in Climate and Space Sciences and Engineering took third.

The goal of the competition was to create a model that would predict whether potential clients would end up getting a mortgage based on the loan product originally offered to them. In order to create this model, each participant was given access to proprietary de-identified financial data from recent QL clients. The accuracy of their models was then evaluated on one month of client data.

Alexander Zaitzeff

“Every time I participate in a competition I try out a new technique,” Zaitzeff said. “MDST puts me in competitions with other U-M students who I can team up with and learn from.”

“This was a very valuable competition because it gives people experience working with real datasets, on actual problems that companies work on day to day,” said Jonathan Stroud, organizational chair of MDST.

Brian Ball, a data scientist at QL and U-M alum, said the input from MDST students gained through the competition helped confirm the company’s hope that “our system is predictable from a mathematical standpoint.”

“In that regard, we can use the results produced and the methods used to drive good decisions to most benefit our clients,” he added. “We view this as a total success as it was our hypothesis — and underlying hope — from the beginning.”

About 20 people from QL’s Data Science team gathered to hear how the MDST winners developed their models, as well as vice presidents of the Business Intelligence unit.

The winning entry was an “ensemble model,” in which several models are synthesized into one predictive framework.

Finding that so many different kinds of models performed similarly was a confirmation that “the data tells the story,” Ball said.

“Allowing for each technique to contribute more strongly to the final score in areas where the model type performs well (referred to as “blending” or “stacking”) is an especially strong method and one we should consider moving forward,” he said.

The competition began in September and ran until the end of the Fall semester. Over 70 students competed in this challenge, including both graduates and undergraduates from several schools and departments across the University.

MDST typically runs two or three competitions each year — the current competition involves predicting the value of NFL free agents, and is being conducted in partnership with the Baltimore Ravens. For more information, please visit MDST’s webpage: midas.umich.edu/mdst

HPC training workshops begin Tuesday, Feb. 13

By | Educational, Events, General Interest, Happenings, HPC, News

series of training workshops in high performance computing will be held Feb. 12 through March 6, 2018, presented by CSCAR in conjunction with Advanced Research Computing – Technology Services (ARC-TS).

Introduction to the Linux command Line
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also known as the “command line.”
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Tuesday, Feb. 13, 1 – 4 p.m. (full descriptionregistration)
• Friday, Feb. 16, 9 a.m. – noon (full description | registration)

Introduction to the Flux cluster and batch computing
This workshop will provide a brief overview of the components of the Flux cluster, including the resource manager and scheduler, and will offer students hands-on experience.
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Monday, Feb. 19, 1 – 4 p.m. (full description | registration)
• Tuesday, March 6, 1 – 4 p.m. (full description | registration)

Advanced batch computing on the Flux cluster
This course will cover advanced areas of cluster computing on the Flux cluster, including common parallel programming models, dependent and array scheduling, and a brief introduction to scientific computing with Python, among other topics.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Wednesday, Feb. 21, 1 – 5 p.m. (full description | registration)
• Friday, Feb. 23, 1 – 5 p.m. (full description | registration)

Hadoop and Spark workshop
Learn how to process large amounts (up to terabytes) of data using SQL and/or simple programming models available in Python, R, Scala, and Java.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Thursday, Feb. 22, 1 – 5 p.m. (full description | registration)

New Data Science Course – Winter 2018

By | Educational, News

Computational Data Science
(EECS 598 / BIOINF 505)

A new graduate course that provides an in-depth introduction to computational methods in data science for identifying, fitting, extracting and making sense of patterns in large data sets is now enrolling students for Winter 2018.

Lectures will typically begin with an introduction of a core data science method, followed by the student programming the method computationally with a computer assisting the student by certifying when the program is correct, interleaved with ‘just-in-time’ theory that will expose the student to the mathematics that underpin the methodology. Once the method has been correctly implemented, the students will be given a real world example or ‘success story’ to work with that illustrates when the algorithm ‘works’ as expected, followed by an instructor guided computational exploration of the various subtleties of the algorithm and its weakness.

A full course description, prerequisites and schedule are available.

Please share this announcement with students who might be interested.

U-M students make strong showing at Michigan Datathon

By | Data, Educational, Events, General Interest, Happenings, News

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

 

2nd-place team:

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

 

For more, see https://lsa.umich.edu/stats/news-events/all-news/graduatenews/MichiganDatathonWinners0.html