Graduate programs in computational and data science — informational sessions Sept. 19 & 21

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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.
Students participate in the 2016 MIDAS Summer Camp on the University of Michigan campus in Ann Arbor.

Raising the next generation of data scientists at the MIDAS Summer Camp

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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.

 

U-M student group wins second-place in National Institute on Drug Abuse mobile app challenge

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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.

Prof. Dragomir Radev teaching online course on Natural Language Processing through Coursera

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This summer, Prof. Dragomir Radev is teaching two course offerings of his course Introduction to Natural Language Processing through Coursera, the online education platform which aims to provide universal access to the world’s best education.

Prof. Radev is offering the 12-week course twice, with the first session having begun July 4th and the second session set to begin on August 1st. The course provides an introduction to the field of Natural Language Processing. It includes relevant background material in Linguistics, Mathematics, Probabilities, and Computer Science.

More details are available on the course website.

U-M, Coursera offer five-course specialization in Applied Data Science with Python

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Coursera and the University of Michigan are offering a five-course specialization in Applied Data Science with Python starting in September. The courses cost $79 each, and students who complete all coursework, including a capstone project, will receive a Certificate.

The courses, taught by U-M faculty members Christopher Brooks (SI), Kevyn Collins-Thompson (SI and EECS), Daniel Romero (SI and EECS) and VG Vinod Vydiswaran (Medical School and SI), are:

  • Introduction to Data Science in Python
  • Applied Plotting, Charting and Data Representation in Python
  • Applied Machine Learning in Python
  • Applied Text Mining in Python
  • Applied Social Network Analysis in Python (Capstone project)

For more information, see the Coursera webpage.

Building a Community of Social Scientists with Big Data Skills: The ICOS Big Data Summer Camp

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As the use of data science techniques continues to grow across disciplines, a group of University of Michigan researchers are working to build a community of social scientists with skills in Big Data through a week-long summer camp for faculty and graduate students.

Having recently completed its fourth annual session, the Big Data Summer Camp held by the Interdisciplinary Committee for Organizational Studies (ICOS) trains approximately 50 people each spring in skills and methods such as Python, SQL, and social media APIs. The camp splits up into several groups to try to answer a research question using these newly acquired skills.

Working with researchers from other fields is a key component of the camp, and of creating a Big Data social science community, said co-coordinator Todd Schifeling, a Research Fellow at the Erb Institute in the School of Natural Resources and Environment.

“Students meet from across social science disciplines who wouldn’t meet otherwise,” said Schifeling. “And every year we bring back more and more past campers to present on what they’ve been doing.”

Schifeling himself participated in the camp as a student before taking on the role of coordinator this year.

Teddy DeWitt, the other co-coordinator of the camp and a doctoral student at the Ross School of Business, added the camp presents the curriculum in a unique way relative to the rest of campus.

“This set of material does not seem to be available in other parts of the university, at least … with an applied perspective in mind,” he said. “So we’re glad we have this set of resources that is both accessible and well-received by students.”

Participants range in skill from beginning to advanced, but even a relatively advanced student like Jeff Lockhart, a doctoral student in sociology and population studies who describes himself as “super-committed to computational social science,” said that it’s hard to find classes in computational methods in social science departments.

“[The ICOS camp] doesn’t expect a lot of prior knowledge, which I think is critical,” Lockhart said.

Lockhart, DeWitt, and Dylan Nelson, also a sociology doctoral student, are working on setting up a series of workshops in Computational Social Science for fall 2016 (contact Lockhart at jwlock@umich.edu for more information). Lockhart said it’s critical that social scientists learn Big Data skills.

“If we don’t have skills like this, there’s no way for us to enter into these fields of research that are going to be more and more important,” he said.

“A lot of the skills we’ve learned are sort of the on-ramp for doing data science,” DeWitt added.

The camp is co-sponsored by Advanced Research Computing (ARC).

Great Lakes Observing System Data Challenge: Call for Issue Experts, Sponsors

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CALL FOR ISSUE EXPERTS AND SPONSORS

The Great Lakes Observing System (GLOS) is hosting the Great Lakes Data Challenge in summer of 2016. As part of our 10 year anniversary, GLOS will be taking open data to the next level by using open innovation to broaden our community and create new partnerships to engage people in problem solving for the Great Lakes. GLOS is currently soliciting support for sponsors and issue experts.

GOALS

  • Inspire a wider audience to engage with Great Lakes issues
  • Use technologies, innovation and creativity to solve Great Lakes problems
  • Encourage the use of open data resources from GLOS and beyond

TIMELINE

  • Late May 2016: Launch challenge
  • June: Kick-off event(s), including IAGLR
  • August 15: Submissions due. Submissions can include an app, data “mash-up”, visualization, story, or other innovative idea for using, collecting, analyzing, visualizing, and/or communicating Great Lakes data.
  • August 15-31: Judging
  • September 15: Winners notified
  • October 12-13: Award presentation at GLOS Annual Meeting in Ann Arbor, MI

GLOS PROVIDES

  • Baseline prize money: $5,000
  • Data, technical support, and resources for developer guidelines, rules, etc.
  • Data Challenge(s) coordination

WE NEED YOU

Sponsors: by May 20 The Great Lakes Data Challenge is a unique opportunity to network the region’s
environmental, governmental and non-profit sectors with the information technology sector. Sponsors must commit by May 20 to ensure inclusion in event promotions.

Consider sponsoring the challenge at one of our suggested levels (see next page) to help support prize
money, event costs, and promotional giveaways. This is a great way to promote your business/organization to a diverse audience of environmental data and technology stakeholders.

Issue Experts: by June 1 We are looking for volunteers with expertise in areas including invasive species, nutrients and algae and boater safety, among others. You would agree to be a resource to teams who have specific questions about the topic at hand. The commitment could be flexible according to your interest and availability.

Please contact GLOS at kpaige@glos.us if you are interested in supporting the data challenge in any of these areas.

Be a part of the Great Lakes Observing System’s Data Challenge

  • SUPERIOR $5,000
    All lower level sponsorship benefits as well as…
    Top billing as Data Challenge co-sponsor in all event promotions and media releases
    Large, prominent logo on event giveaways, promotional signage, and website
  • MICHIGAN $2,500
    All lower level sponsorship benefits as well as…
    Acknowledgement as co-sponsor for a custom challenge category
    Logo on event giveaways
  • HURON $1,000
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement at promotional events including kick-off and award presentation
    Logo on Data Challenge website and promotional signage
  • ONTARIO $500
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement on promotional signage
    Complimentary individual (for 1 person) GLOS membership and registration to the GLOS Annual Meeting
  • ERIE $250
    Sponsorship acknowledgement and website link on Data Challenge website
    Acknowledgement in GLOS Annual Report

Link to sponsor commitment page.

New graduate course offering: “Methods and Practice of Scientific Computing”

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The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce “Methods and Practice of Scientific Computing”, the first graduate course designed and organized by MICDE faculty. The course will be taught in Fall 2016, coordinated by Dr. Brendan Kochunas. This foundational course in scientific computing has been developed as a broad introduction to the subject, and has been designed to support research in all disciplines represented in MICDE. In addition to Brendan Kochunas, the course was developed by MICDE professors Bill Martin, Karthik Duraisamy, Vikram Gavini, and Shravan Veerapaneni, and MICDE Assistant Director Mariana Carrasco-Teja.

The details follow:

NERS 590
4 credits
Prerequisites: Graduate standing and permission of instructor.

This course is designed for graduate students who are developing the methods, and using the tools, of scientific computing in their research. With the increased power and availability of computers to do massively scaled simulations, computational science and engineering as a whole has become an integral part of research that complements experiment and theory. This course will teach students the necessary skills to be effective computational scientists and how to produce work that adheres to the scientific method. A broad range of topics will be covered including: software engineering best practices, computer architectures, computational performance, common algorithms in engineering, solvers, software libraries for scientific computing, uncertainty quantification, verification and validation, and how to use all the various tools to accomplish these things. The class will have lecture twice a week and have an accompanying lab component. Students will be graded on homeworks, lab assignments, and a course project.

A draft of the syllabus can be found here. Please contact MICDE at micde-contact@umich.edu with any questions.

Call for Participation: BioCreative conference seeks papers/presentations — May 5 deadline

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The BioCreative 2016 in Corvallis, Oregon, USA, on August 1-2, 2016 this year will run jointly with the Annual International Conference on Biological Ontology (ICBO), August 1-4, 2016, and both events will have shared sessions and invited speakers on August 2, 2016. The aim is to foster discussion, exchange, and innovation in research and development in the areas of text mining and Biomedical Ontology (including plants, agriculture, environment and biomes).

BioCreative 2016 solicits papers/presentations for the following topics/sessions:

  1. Text mining-facilitated models of curation, Lynette Hirschman and  Rezarta Islamaj Dogan, on application of text mining methods in areas such as crowdsourcing, database curation, publication process, and metagenomics.
  2. Text mining in precision medicine, Zhiyong Lu, Martin Krallinger and Fabio Rinaldi, on methods for annotations such as disease, phenotype, and adverse reactions in different text sources literature, clinical records and social media
  3. Domain portability or generalizability across medical literature, Donald Comeau and Kevin Bretonnel Cohen, on methods to achieve interoperability, generalizability and scalability in text mining: BioC, RDF and semantic web, among others
  4. Text mining and ontologies, Cecilia Arighi and ICBO, on application of ontologies in text mining, and text mining as ontology builder.

Submission: http://icbo.cgrb.oregonstate.edu/Biocreative_submission

Important dates:

May 5, 2016:               2-page extended abstract submission deadline

May 25, 2016:             Author notification

July 1, 2016:               Camera-ready copies deadline

August 1-2, 2016:        BioCreative 2016

Travel awards:

Funds are available for US participants for the amount up to $700 to participate in BioCreative workshop 2016. To apply complete the application by May 5. Women, under-represented minorities, students, and post-doctoral fellows are encouraged to apply.

For more details, please check: http://icbo.cgrb.oregonstate.edu/Biocreative_submission