Transportation

With the advent of driverless vehicles and ride-sharing in an age of continued urbanization, climate change and pollution, we are undoubtedly at the brink of the next transportation revolution. Data science is at the center of this revolution. The collection of data on transportation and driver behavior is no longer a bottleneck; our current challenge is to develop sophisticated data analysis and interpretation that impact the design of future transportation to address challenges including automation, climate change and urban inequality. The MIDAS Transportation Research Hub aims to position U-M researchers at the forefront of our nation’s Big Data transportation research, develop innovative methods and tools, and apply the insight to solve real-world transportation challenges.  The MIDAS Data-Intensive Transportation Research Hub currently funds two projects.

Learning Analytics

Three questions have stirred controversies, fueled perpetual debates, and polarized educators, parents and students for as long as educational institutions have existed: What constitutes good teaching? What are effective learning practices? And, perhaps most importantly, what does “student achievement” mean? Attempts to answer these questions have evolved from philosophical musings to data-based modern educational research. Today, Big Data research adds to our power as we unravel the secret of successful learning and teaching .  The MIDAS Learning Analytics Research Hub is a new addition to U-M’s nationally renowned research in learning analytics.  MIDAS funds two initial core research projects.

Health Science

The unprecedented wealth of health data brings unprecedented opportunities and challenges to improve healthcare.  On the most microscopic level, we are now examining the genome one cell at a time, monitoring in real-time an individual’s health behavior, and prescribing medication based on the genetics and chemistry of each individual.  On the most macroscopic level, we can combine dozens of clinical record data for one patient, and can analyze the health data of an entire nation.  However, our ability to gain insight into such vast data lags significantly behind our ability to accumulate data.  The MIDAS Data-Intensive Health Science Research Hub aims to position UM researchers as national leaders as biomedical science embraces the opportunities and challenges brought about by advances in data science, catalyze the development of theories and innovative methodologies in data science, and their application to the entire spectrum of biomedical science.  The hub currently funds three projects.

Data Science for Music

When you listen to a Beethoven piano concerto, pick a song or channel to listen to on the web, send your kids to music lessons, or donate to your local symphony orchestra, do you think of data science? Unlikely. But data science is increasingly critical in the creative process and commercial activities of music. Using Big Data analytics, online music platforms tailor individualized song recommendations for their users; music theorists search for defining features in different types of music; computer scientists develop algorithms that can compose music in any style; behavioral scientists determine how to best engage concert goers; the list is endless. MIDAS recently established the Data Science for Music Research Hub and currently funds four projects.

Project Filter

Use this box to search MIDAS Funded Projects by name, faculty member, or other keyword. Use the filters below to search by major data science methodologies or applications.

Applications

Methodologies

Building a Transportation Data Ecosystem

Data Science and Human Endeavor, Data Science and Our Habitat, Data Science and Our Society

Computational Approaches for the Construction of Novel Macroeconomic Data

Data Science and Human Endeavor, Data Science and Our Society

Data Science for Quantum Simulation

Data Science and the Physical World