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

By | Educational, General Interest, News

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

By | Educational, General Interest, News

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.

MIDAS Challenge Initiative Awards featured on Xconomy news website

By | General Interest, News

The website has featured transportation data science projects awarded funding under the first round of the MIDAS Challenge Initiatives.

College of Engineering Prof. Pascal Van Hentenryck and Carol Flannagan of the U-M Transportation Research Institute were awarded $1.25 million each for projects that will apply data science methodologies to transportation. U-M Dearborn is contributing another $120,000 to each project, and co-PIs are involved from the School of Information, Medical School, Architecture and Urban Planning, Computer Science, School of Public Health, and the Institute for Social Research, among others.

Please see the Xconomy article for more.

Toyota Materials Handling North America seeks proposals for University Research Program in supply chain, logistics and material handling — Aug. 31 submission deadline

By | Funding Opportunities, News, Translational

Toyota Materials Handling North America is accepting proposals for sponsored research under its University Research Program, which aims to “drive the next generation of technology for the entire supply chain, logistics and material handling industry.”

According to the company’s description, “The mission is to encourage professors and researchers to apply their knowledge of engineering and technical fields, drawing synergies and collaboration between collegiate research and Toyota Material Handling North America.”

Visit for more information and submission instructions.

Ivo Dinov interviewed by NOVA/PBS

By | General Interest, News

Ivo Dinov was interviewed by NOVA/PBS to discuss a recent PNAS Report identifying significant potential shortfalls of Big Data functional magnetic resonance imaging (fMRI) studies, of which there may be over 35,000 in the past 25 years.

The article used 500 normal control subjects (null data) to generate 3 million simulation studies where every experiment included randomly chosen subjects, either resting state or task activation fMRI, and found false-positive discoveries (significant grouping effects where there were none) in up to 70 percent of the simulations.

Although this does not discredit any specific previously published fMRI findings, the investigations suggests the need for novel Big Data analytics methods, and scalable software tools, that can reduce the false-positive rate.

Intelligent Transportation Systems: IEEE ITSS Newsletter, July 2016

By | Uncategorized


IEEE ITS Society Newsletter, Vol. 18, No. 3, July 2016
The latest issue of the ITSS Newsletter is now available for download.

In This Issue: 

Society News and Announcements:

- Message from the Editor
- Message from the IEEE ITS President
- Join the IEEE ITS Society
- 2016 IEEE ITS Award Announcement
- 2016 IEEE ITSS Best Ph.D. Dissertation Award Call for Application
- IEEE ITSS Educational Activities Website launching!
- ITS Podcast New Episodes and Information
- Job Announcement


Conferences and Call for Papers:

- IEEE ITSC2016 Workshops - CFP
- IEEE Transactions on Intelligent Vehicles — CFP
- S. Issue on “Autonomous Cooperative Driving” — CFP IEEE ITS



- S. Issue on “'High Performance Computing in Simulation and Optimization
of Dynamic Transportation Networks” — CFP IEEE ITS Magazine
- S. Issue on “Holistic approaches for Human-Vehicle Systems: combining models, interactions and control” — CFP IEEE Transactions on HMS
- 1st EAI Int. Conf. on Security & Privacy in Vehicular Networks — CFP
- Workshop on ITS and Smart Mobility—Call for Ext. Abstracts
- Special Session on Intelligent Cooperative Driving, and Autonomous


Connected Vehicles (ICD & ACV 2016) — CFP

Conference Calendar

Forthcoming papers at Transactions on ITS

Forthcoming papers at ITS Magazine

Most popular papers/episode at T-ITS, ITS Magazine and ITS Podcast

Officers and Committee Chairs

Editorial Board

NL Archive and Subscription
Javier Sanchez-Medina
Associate Editors
Haluk Eren
Conferences Workshops and Journals
The IEEE ITS Society Newsletter is published quarterly in January, April, July, and October. In each month without a full Newsletter you will receive a Digest like this one.
All past issues of the Newsletter may be downloaded at no charge from the Society’s web site:
You can also UNSUBSCRIBE here:

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

By | Educational, Feature, General Interest, News

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 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).