The Big Data in Transportation and Mobility symposium held June 22-23, 2017, in Ann Arbor, MI brought together more than 150 data science practitioners from academia, industry and government to explore emerging issues in this expanding field.
Sponsored by the NSF-supported Midwest Big Data Hub (MBDH) and the Michigan Institute for Data Science (MIDAS), the symposium featured lightning talks from transportation research programs around the Midwest; tutorials and breakout sessions on specific issues and methods; a poster session; and a keynote address from two representatives of the Smart Columbus project: Chris Stewart, Ohio State University Associate Professor of Computer Science and Engineering, and Shoreh Elhami, GIS Manager for the city of Columbus.
Speakers and attendees came from a number of organizations from across the midwest including the University of Michigan, University of Illinois, University of Nebraska, University of North Dakota, North Dakota State University, Ohio State University, Purdue University, Denso International America, Fiat Chrysler, Ford Motor Company, General Motors, IAV Automotive Engineering and Yottabyte.
“This was an extremely valuable opportunity to share information and ideas,” said Carol Flannagan, one of the organizers of the symposium and a researcher at MIDAS and the U-M Transportation Research Institute. “Cross-discipline and cross-institutional collaboration is crucial to the success of Big Data applications, and we took a significant step forward in that vein during this symposium.”
Topics addressed in talks, breakouts, and tutorials included:
New Analytic Tools for Designing and Managing Transportation Systems
New Mobility Options for Small and Mid-sized Cities in the Midwest
Automated and Connected Vehicles
Transforming Transportation Operations using High Performance Computing
Using Big Data for Monitoring Bridges
At the closing session, participants outlined some areas that could be fruitful to focus on going forward, including increasing data-science literacy in the general public; diversity and workforce development in data science; public data-sharing platforms and partners; and privacy issues.
For a complete list of speakers and topics, please see the agenda. Videos of selected talks will be posted at midas.umich.edu in the coming days.
The University of Michigan is beginning the process of building our next generation HPC platform, “Big House.” Flux, the shared HPC cluster, has reached the end of its useful life. Flux has served us well for more than five years, but as we move forward with replacement, we want to make sure we’re meeting the needs of the research community.
ARC-TS will be holding a series of town halls to take input from faculty and researchers on the next HPC platform to be built by the University. These town halls are open to anyone and will be held at:
College of Engineering, Johnson Room, Tuesday, June 20th, 9:00a – 10:00a
3114 Med Sci I, Wednesday, June 28th, 2:00p – 3:00p
Your input will help to ensure that U-M is on course for providing HPC, so we hope you will make time to attend one of these sessions. If you cannot attend, please email email@example.com with any input you want to share.
Advanced Research Computing – Technology Services (ARC-TS) has an exciting opportunity for a Research Cloud Administrator.
This position will be part of a team working on a novel platform for research computing in the university for data science and high performance computing. The primary responsibilities for this position will be to develop and create a resource sharing environment to enable execution of Data Science and HPC workflows using containers for University of Michigan researchers.
The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.
The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.
To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.
CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.
The Michigan Institute for Data Science (MIDAS) is convening a research working group on mobile sensor analytics. Mobile sensors are taking on an increasing presence in our lives. Wearable devices allow for physiological and cognitive monitoring, and behavior modeling for health maintenance, exercise, sports, and entertainment. Sensors in vehicles measure vehicle kinematics, record driver behavior, and increase perimeter awareness. Mobile sensors are becoming essential in areas such as environmental monitoring and epidemiological tracking.
There are significant data science opportunities for theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.
Our working group welcomes researchers with interest in mobile sensor analytics in any scientific domain, including but not limited to health, transportation, smart cities, ecology and the environment.
Where and When:
Noon to 2 pm, April 13, 2017
School of Public Health I, Room 7625
Brief presentations about challenges and opportunities in mobile sensor analytics (theory and application);
A brief presentation of a list of funding opportunities;
Discussion of research ideas and collaboration in the context of grant application and industry partnership.
Future Plans: Based on the interest of participants, MIDAS will alert researchers to relevant funding opportunities, hold follow-up meetings for continued discussion and team formation as ideas crystalize for grant applications, and work with the UM Business Engagement Center to bring in industry partnership.
Please RSVP. For questions, please contact Jing Liu, Ph.D, MIDAS research specialist (firstname.lastname@example.org; 734-764-2750).
The National Academies Committee on Applied and Theoretical Statistics has released proceedings from its June 2016 workshop titled “Refining the Concept of Scientific Inference When Working with Big Data,” co-chaired by Alfred Hero, MIDAS co-director and the John H Holland Distinguished University Professor of Electrical Engineering and Computer Science.
The workshop explored four key issues in scientific inference:
Inference about causal discoveries driven by large observational data
Inference about discoveries from data on large networks
Inference about discoveries based on integration of diverse datasets
Inference when regularization is used to simplify fitting of high-dimensional models.
The workshop brought together statisticians, data scientists and domain researchers from different biomedical disciplines in order to identify new methodological developments that hold significant promise, and to highlight potential research areas for the future. It was partially funded by the National Institutes of Health Big Data to Knowledge Program, and the National Science Foundation Division of Mathematical Sciences.
The Michigan Data Science Team and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit on a data challenge that seeks to answer the question: How can blight ticket compliance be increased?
An organizational meeting is scheduled for Thursday, Feb. 16 at 5:30 p.m. in EECS 1200.
The city is making datasets available containing building permits, trades permits, citizens complaints, and more.
From left, Al Hero, U-M; Patrick Wolfe, UCL; and Brian Athey, U-M signed an agreement for research and educational cooperation between the University of Michigan and University College London.
ANN ARBOR, MI and LONDON — The Michigan Institute of Data Science (MIDAS) at the University of Michigan and the Centre for Data Science and Big Data Institute at UCL (University College London) have signed a five-year agreement of scientific and academic cooperation.
The agreement sets the stage for collaborative research projects between faculty of both institutions; student exchange opportunities; and visiting scholar arrangements, among other potential partnerships.
“There is a lot of common ground in what we do,” said Patrick Wolfe, Executive Director of UCL’s Centre for Data Science and Big Data Institute. “Both MIDAS and UCL cover the full spectrum of data science domains, from smart cities to healthcare to transportation to financial services, and both promote cross-cutting collaboration between scientific disciplines.”
Alfred Hero, co-director of MIDAS and professor of Electrical Engineering and Computer Science at U-M, said that one of the original goals of the institute when it was founded in 2015 under U-M’s $100 million Data Science Initiative was to reach out to U.S. and international partners.
“It seemed very natural that this would be the next step,” Hero said, adding that it would complement MIDAS’s recent partnership with the Shenzhen Research Institute of Big Data in China. “UCL epitomizes the collaboration, multi-disciplinarity and multi-institutional involvement that we’re trying to establish in our international partnerships.”
Wolfe visited Ann Arbor in early January to sign a memorandum of understanding along with Hero and Brian Athey, professor of bioinformatics and the other MIDAS co-director.
The agreement lists several potential areas of cooperation, including:
FLINT—A partnership between Google and the University of Michigan’s Flint and Ann Arbor campuses aims to provide a smartphone app and other digital tools to Flint residents and officials to help them manage the ongoing water crisis.
The app and other tools will help predict where lead levels will be highest in the city’s water, and they’ll pull together information and resources to make the crisis easier to navigate for those affected. The project is made possible by a $150,000 grant from Google.
“This investment by Google is an outstanding commitment to our community. It creates an ideal combination of an industry powerhouse with faculty expertise. It will create new opportunities for students and continue building community partnerships—all so that we can provide quick and critically important information and analysis for our community as we move forward,” said Chancellor Susan E. Borrego of the University of Michigan-Flint.
The Android app is slated for roll-out this summer. It could help residents determine whether their homes are at high risk of having lead-contaminated water. It could also help them locate day-to-day resources for lead testing, water distribution, water bottle recycling, water filters, and volunteer opportunities. A website will offer similar resources and will be accessible on any computer, including those in public libraries.
Additional web-based tools for researchers and government officials could provide detailed insight on how to deploy repairs and resources. For example, they could help identify and prioritize the water service line replacements.
A student team at UM-Flint has already developed a prototype smartphone app for Flint residents. Google and U-M Ann Arbor will work with them through the spring and summer to add mapping features that use predictive analytics from U-M Ann Arbor’s Michigan Data Science Team. The team will also develop an improved user interface with assistance from Google.
Google has pledged a variety of resources to the project including a grant and remote and on-site assistance from its user experience and app development team. The company will also donate data resources to the Michigan Data Science Team including mapping, satellite imagery, and geo-location data.
Initial work by the data science team has already shown some success at predicting which homes and neighborhoods have a high risk of lead contamination. In the coming months, they’ll continue to apply predictive algorithms and machine learning techniques to data from a wide variety of sources including Google, the State of Michigan and the City of Flint. The data includes existing lead testing data; detailed information on the type and location of water infrastructure; and information on the size, age, type, and condition of every parcel of property in the city.
“There’s a lot of data on the water crisis, but it’s scattered over many different agencies and places,” said Jacob Abernethy, an assistant professor of computer science and engineering at U-M Ann Arbor and faculty advisor to the Michigan Data Science Team. “By organizing it in one place and analyzing it, we can predict which areas are likely to be at risk. We can help planners determine which infrastructure repairs will benefit the most residents, and how to allocate resources like bottled water most efficiently.”
Google and U-M also plan to create a separate set of web tools for city planners and other officials. They will include extensive mapping and predictive analytics, with details on waterline type and location and other infrastructure data.
Mark Allison is an assistant professor of computer science at UM-Flint and the faculty leader of the Flint student team. He says the project will be an opportunity for students to make a difference in the water crisis and pick up valuable real-world development experience along the way.
“Finding the best way to put resources close to where high lead levels are is a big part of managing this crisis, and it’s the kind of problem that analytics can solve. We also want to give residents more transparency by making it easier for anyone to get access to the most up-to-date information,” Allison said. “I think this project will be transformative. And for all of us here in Flint, it’s about much more than grades.”
Allison said the team is working to keep the tools they develop flexible, enabling them to be used by other cities that face similar crises. His team is developing the tools as part of UM-Flint Computer Science’s community-based learning program, which puts students to work on real-world challenges in and around Flint.
The Michigan Data Science Team is a competitive extra-curricular team at U-M Ann Arbor. Founded by Abernethy, the team builds and applies advanced computer algorithms that can analyze and “learn” from large sets of data. By finding connections and patterns within that data, they can make predictions about future events. The techniques are already widely used in areas like online retailing and advertising.
“Access to clean drinking water is a concern all over the world, but in the United States it’s often a foregone conclusion. That is not the case recently for the residents of Flint, Michigan,” said Mike Miller, head of Google Michigan. “I am proud that we can contribute to help with the recovery of and we hope we can help to support a resolution to this crisis and get the residents of Flint the resources and respect they so rightly deserve.”
The Flint Water crisis began after April of 2014, when the city’s drinking water source was changed from Lake Huron via Detroit’s water system to the Flint River. The water supply was not properly monitored for corrosion control and it caused lead to leach from service lines into the city’s drinking water. While the city has since switched its water supply back to the Detroit system, residents are still being advised not to drink unfiltered tap water.