Four research teams from the University of Michigan and Shanghai Jiao Tong University (SJTU) in China are sharing $800,000 in awards to study depression, electric vehicles, urban green space and bone cancer.

Since 2010, the two universities have collaborated on a number of research projects that address challenges and opportunities in energy, biomedicine, nanotechnology and data science. In the latest round of annual grants in 2016, the winning projects focus on data science and how it can be used to sustain critical infrastructures for the environment and human health.

The program funds projects that have commercial potential and are likely to attract follow-on research funding from the U.S. and Chinese governments, as well as industry.

For more about the SJTU collaboration, visit the UMOR website. To contact the program, email

Funded Projects

Intern Health Study — Mobile Health/Medical Education

Developing a better understanding of how life stress leads to the onset of psychiatric disorders, such as major depression, has the potential to transform our ability to prevent and treat these disorders. At the completion of this project, it is our expectation that we will have identified mobile signatures that change with stress and prospectively predict changing levels of depressive symptoms. Further, it is our expectation that we will establish a nimble platform well positioned to rapidly assess new mobile electronic assessment and intervention technologies as they emerge.

Adaptation and Coordination Technology of Large Scale EV Charging and Variable Renewable Energy Based on Big Data and Electricity Network Reliability Analysis

Production capacity of electric vehicles and plug-in hybrid electric vehicles is expected to reach 2 million per year in China, which is close to 10 percent of the current annual car sales. This emerging demand because of unanticipated peaks and variability can pose a significant risk to the stability of the grid. This proposal is aimed at turning the challenge into an opportunity by bringing together a team of researchers with expertise in algorithms for electronic commerce and for managing variable demand, distributed systems and security or privacy. The key accomplishment will include laying the foundation for leadership in the area of a data-driven approach for models, algorithms and mechanism design to incentivize users to charge vehicles at appropriate times and locations, leading to better load management, a more reliable grid, and even time and cost savings for end-users especially under high penetration of distributed renewable energy.

Unlocking the Potential of Big Data from Mobile Phones to Assess Urban Green Space Utilization

Urban green space has been associated with a wide range of health benefits, but much of the pertinent evidence has relied on labor-intensive, direct observation of utilization, self-reported health indicators or experiments in artificially controlled environmental conditions. We propose to fill in such knowledge gaps by making use of the massive data of mobile phone locations. Our project aims to examine the spatial-temporal utilization of urban green space, interpret the recreational behaviors from the mobile phone location data and investigate how the characteristics of urban green space affect recreational behaviors.

Establishment of Metabolomic-Based Osteosarcoma Prognostic Model using Non-Convex Kernel Models

Osteosarcoma is the most common primary bone malignancy and most genomically complex cancer. Its five-year survival has remained unchanged in the past 30 years, and early occurrence of pulmonary metastasis is the main challenge for patients to be cured. This project aims at establishing a prognostic predictive model using machine learning in osteosarcoma metabolomics. This model will be potentially useful for providing adjuvant information to stratify osteosarcoma patients and subsequently to guide clinical decision.