Since 2009, the University of Michigan and Shanghai Jiao Tong University (SJTU) in China 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 grant awards, five research teams are sharing $1 million to study air quality, galaxy clusters, lightweight metals, financial trading, and renewable energy, all from a data-intensive perspective.

In 2016, four research teams shared $800,000 in awards to study depression, electric vehicles, urban green space and bone cancer.

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 UMSJTU2016@umich.edu.

More information on the funded projects described below is available by clicking on the image or description.

Funded Projects


2017

Learning Impact of Air Pollution on Kidney Disease Epidemiology in U.S. and East China Using Big Public Health Data

Incidence rate of end stage renal disease (ESRD) and the mortality rate among ESRD patients are high in both China and the U.S. At the same time, air quality in China has been worsening and often reaches or exceeds hazardous levels in many regions. Researchers will assess and contrast the effect of the exposure level to the air pollution on the ESRD patients’ mortality and morbidity between the two countries to provide data-driven analytics for public health policymaking.

A weather-process and machine learning combined approach to improve solar forecast for PV power generation

Photovoltaic (PV) energy is a major potential source of renewable energy for future massive integration with the electricity grid. Since variations of solar irradiation over time directly affect the PV output from solar panels, accurate forecasting of the solar irradiance impinging on solar panels is critical to efficient grid integration and power management. Researchers will work on a hybrid approach for the intra-day forecast for PV production using data-driven algorithms guided by known physics and checked against the observations, as well as the ensembles of simulations from state-of-the-art weather research and forecast model.

Constraining Cosmological Parameters with Galaxy Clusters: A New Meta-analysis Approach to Cosmology

Researchers have evidence that about 7 billion years after the Big Bang, the expansion of the universe stopped slowing down and then started to speed up. Researchers are conducting a novel analysis to shed light on what started this acceleration, how it evolved and how it will end.

Automatic construction of a causality knowledge base from large online financial text

Perceptions of the relationships between causes and effects plays a critical role in people’s daily behavior and decision-making, and are of great interest in many domains, including finance, where understanding causal relationships can provide significant opportunities for economic benefits. This project aims to build a financial causality knowledge base by analyzing a large corpus of online text trying to capture the causal strength of different finance-related events. The rules in this knowledge base can be used to predict financial events and generate alerts in financial trading.

Development of a Data-based Alloys Design Methodology and Application to Magnesium

The goal of this project is to develop a data‐science approach for alloy design and apply it to Magnesium (Mg) alloys. Researchers will build an artificial neural network (ANN) model to establish the relationships between material composition, microstructural features and mechanical properties. They will train the ANN model first for commercial and other well‐characterized Mg alloys, and then extend it to a wider range of composition and processing histories. To this end, researchers will generate physics‐based simulation data to inform the model regarding the effects of various alloying elements and processing conditions, along with their prediction uncertainties. Finally, a few new Mg alloys with superior mechanical properties will be identified for potential commercialization using the model.

2016

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.