NOVEMBER 15, 2016
RACKHAM BUILDING, 915 E. WASHINGTON ST., ANN ARBOR
8 a.m. — Registration / Coffee
8:30 a.m. — Welcome, Eric Michielssen, Associate Vice President, Advanced Research Computing
|Eric Michielssen is the Associate Vice President, Advanced Research Computing, the Louise Ganiard Johnson Professor of Engineering, and Professor of Electrical Engineering and Computer Science, U-M College of Engineering|
8:40 a.m. — MIDAS: The Year in Review, Co-Directors Al Hero and Brian Athey
9:15 a.m. — KEYNOTE: Robert Groves, Georgetown University
10:15 a.m. — Panel: Big Data, An International Perspective
This panel, moderated by Al Hero, will bring together data scientists from around the world to address a global point of view on the advancements and potential new applications of data science. Each panelist will speak for approximately 25 minutes before a panel discussion.
Noon — Refreshments, Poster Session
1:30 p.m. — Panel: Data Science Methodologies
This panel will address a variety of methodological topics in data science, from the perspectives of social science, transportation, and learning analytics. Each panelist will speak for approximately 25 minutes before a panel discussion.
3:15 p.m. — Panel: Data Science in Health Research
This panel will address data science in health research through the prisms of bioengineering, biostatistics, and genomics.
5 p.m. — Poster Session
The poster session will feature the research of MIDAS Affiliated Faculty, students enrolled in the Data Science Graduate Certificate Program, other U-M researchers involved in data science, and industry partners. Please note: the poster session will take place in the Michigan League.
NOVEMBER 16, 2016
MICHIGAN LEAGUE, 911 N. UNIVERSITY AVE., ANN ARBOR
Livestream link: goo.gl/9AiJbw
8 a.m. — Registration / Coffee
8:30 a.m. — KEYNOTE: Sudip Bhattacharjee, U.S. Census Bureau
9:30 a.m. — Panel: Data Science in the Social Sciences
This panel will address issues in data science in the social sciences, including political science, communications, and marketing. Each panelist will speak for 25 minutes, followed by a panel discussion.
11:45 a.m. — Refreshments, Poster Session
1:15 p.m. — Panel: Data Science in Transportation
This panel, moderated by Al Hero, will describe two projects in transportation from U-M researchers supported by MIDAS Challenge Initiative funding, and include a presentation from the Chinese carshare service Didi Chuxing. Each panelist will speak for 25 minutes; a discussion will follow.
|Carol Flannagan, U-M Transportation Research Institute
Title: Building a Transportation Data Ecosystem for Data Science Research and Applications
Abstract: This talk will describe a 3-year effort to develop a transportation data ecosystem and analytical methods that support and advance the use of very large transportation datasets to understand human behavior. Our project focuses on three broad aims: 1) Build a transportation data ecosystem on a parallel, distributed computing platform that is optimized to support a variety of Big Data analytical methods and data integration across a variety of data sources. 2) Develop Big Data analytical methods to identify “events of interest” and to separate out driver, environment and situation effects on driving. 3) Develop and implement methods of information integration. Transportation datasets come from many sources and contain different levels of information. For example, driving can occur at the micro-level (how is the vehicle moving?), the tactical level (what level of braking and steering is the driver calling for?), or the macro-level (where is the driver trying to go? Will he/she stop before turning? Is he/she distracted?)
The applications and integrated ecosystem will be designed to support these and many more analyses and applications. We plan for this to serve as a resource for the UM data science community to help solve big problems in transportation in the 21st century.
Bio: Carol A. C. Flannagan is a research associate professor in UMTRI’s Biosciences Group, and director of CMISST. She joined UMTRI in 1991 after completing her Ph.D. in mathematical and experimental psychology at the University of Michigan (U-M). She also holds an M.A. in applied statistics from U-M and a B.A. in psychology from St. Lawrence University.
Dr. Flannagan has over 20 years of experience conducting data analysis and research on injury risk related to motor vehicle crashes and was responsible for the development of a model of injury outcome that allows side-by-side comparison of public health, vehicle, roadway and post-crash interventions. She has also applied statistical methods to understanding of the potential benefits of crash-avoidance technologies, and works to develop novel applications of statistics to improve understanding of transporation safety. Dr. Flannagan’s current work with CMISST involves the fusion and analysis of large state-level crash databases, which are useful in analyzing the effect of a variety of countermeasures on crash involvement and injury risk. In addition, her group is working to make data available to researchers to expand the community of experts in transportation data analysis.
|Pascal Van Hentenryck, U-M College of Engineering
Title: Reinventing Public Urban Transportation and Mobility
Abstract: Ubiquitous connectivity, together with significant advances in autonomous vehicles, intelligent transportation and asset management systems, have the potential to revolutionize public urban transportation and mobility in the coming decade. A new generation of public urban transportation systems can not only mitigate congestion, decrease environmental impacts, reduce costs, and improve service levels; It can also open new mobility markets for the automotive industry, bring a step change in mobility for the poor, the disabled, and the elderly, help rejuvenate inner cities and distressed neighborhoods, and bring health and social benefits that could not be envisioned until recently.
This project pushes this vision by designing novel data-driven urban transportation systems and building the descriptive, predictive, and prescriptive technologies to power them. The optimal design of these urban transportation systems will be informed by models for travel demand, accessibility, driver behavior, and transportation networks. These descriptive and predictive models will be derived by mining and fusing the rich and large data sets newly available and calibrated through interventions and machine learning. The envisioned transportation systems will be operated using real-time optimization algorithms and innovative coordinated traffic assignments to mitigate congestion, maximize network capacity utilization, and improve safety. The project will not only optimize costs, greenhouse emissions, and convenience; It will also strive to boost mobility for entire population segments and transform how to plan and manage a transportation infrastructure optimized for 20th century notions of human mobility. The project is supported by a truly multi-disciplinary team from four colleges, the University of Michigan Transportation Research Institute (UMTRI), and the CDC Injury Center. The team has a history of innovation and deployment in intelligent transportation and asset management systems. It brings significant expertise in data science, from descriptive analytics to predictive and prescriptive analytics and interventions, and in the underlying economic and social mechanisms that are critical to successful deployments.
Bio: Pascal Van Hentenryck is the Seth Bonder Collegiate Professor of Engineering at the University of Michigan. He is a professor of Industrial and Operations Engineering, a professor of Electrical Engineering and Computer Science, and a core faculty in the Michigan Institute of Data Science. Van Hentenryck’s current research is at the intersection of optimization and data science with applications in energy, transportation, and resilience. He is a fellow of INFORMS, a fellow of AAAI, and the recipient of two honorary degrees. He was awarded the 2002 INFORMS ICS Award for research excellence in operations research and compute science, the 2006 ACP Award for research excellence in constraint programming, the 2010-2011 Philip J. Bray Award for Teaching Excellence at Brown University, and a 2013 IFORS Distinguished Speaker award. He is the author of five MIT Press books and has developed several optimization systems that are widely used in academia and industry.
|Jiepeng Ye, Didi Chuxing
Title: Big Data at Didi Chuxing
Abstract: Didi Chuxing is the largest ride-sharing company providing transportation services for over 300 million users in China. Every day, Didi’s platform generates over 70TB worth of data, processes more than 9 billion routing requests, and produces over 13 billion location points. In this talk, I will show how AI technologies including machine learning and computer vision have been applied to analyze such big transportation data to improve the travel experience for millions of people in China.
Bio: Dr. Ye is an associate director of Didi Research Institute. He is an associate professor of DCMB & EECS, University of Michigan. His research focuses on developing machine learning and data mining methods to analyze large-scale, high-dimensional, heterogeneous and complex data.
3:15 p.m. — Panel: Data Science in Learning Analytics
This panel, moderated by Henry Kelly, MIDAS industry liaison, will describe several data science projects going on at U-M in the field of learning analytics, including two projects receiving support through the MIDAS Challenge Initiatives. Each panelist will speak for 25 minutes, followed by a discussion.