Adriaens

Peter Adriaens

By | | No Comments

Prof. Adriaens’ research focuses on the use of data science to uncover trends and features in a range of financial (‘fintech’) applications relevant to economic development and investments aimed at catalyzing sustainable growth, including:
1. Network mapping to query relations in financial networks using visualization techniques
2. Trend and features prediction of value capture and investment grade in startup business models, using machine learning, natural language processing, and decision tools
3. Asset risk pricing of stocks exposed to water risk in their supply chains, using statistical methods, and portfolio theory predictions
4. Financial risk modeling of multi-asset investment funds to drive low carbon economies, leveraging network mapping, and machine learning.

 

Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

Structure of financial data-driven industry ecosystems following relational network mapping and network theory application.

xu-small

Ming Xu

By | | No Comments

My research focuses on developing and applying computational and data-enabled methodology in the broader area of sustainability. Main thrusts are as follows:

1. Human mobility dynamics. I am interested in mining large-scale real-world travel trajectory data to understand human mobility dynamics. This involves the processing and analyzing travel trajectory data, characterizing individual mobility patterns, and evaluating environmental impacts of transportation systems/technologies (e.g., electric vehicles, ride-sharing) based on individual mobility dynamics.

2. Global supply chains. Increasingly intensified international trade has created a connected global supply chain network. I am interested in understanding the structure of the global supply chain network and economic/environmental performance of nations.

3. Networked infrastructure systems. Many infrastructure systems (e.g., power grid, water supply infrastructure) are networked systems. I am interested in understanding the basic structural features of these systems and how they relate to the system-level properties (e.g., stability, resilience, sustainability).

A network visualization (force-directed graph) of the 2012 US economy using the industry-by-industry Input-Output Table (15 sectors) provided by BEA. Each node represents a sector. The size of the node represents the economic output of the sector. The size and darkness of links represent the value of exchanges of goods/services between sectors. An interactive version and other data visualizations are available at http://complexsustainability.snre.umich.edu/visualization

A network visualization (force-directed graph) of the 2012 US economy using the industry-by-industry Input-Output Table (15 sectors) provided by BEA. Each node represents a sector. The size of the node represents the economic output of the sector. The size and darkness of links represent the value of exchanges of goods/services between sectors. An interactive version and other data visualizations are available at complexsustainability.snre.umich.edu/visualization

lynch-small

Jerome Lynch

By | | No Comments

Our group works at the forefront of deploying large-scale sensor networks to the built environment for monitoring and control of civil infrastructure systems including bridges, roads, rail networks, and pipelines; this research portfolio falls within the broader class of cyber-physical systems (CPS). To maximize the benefit of the massive data sets we collect from operational infrastructure systems, we undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, our algorithmic work is focused on the use of statistical signal processing, pattern classification, machine learning, and model inversion/updating techniques to automate the interrogation sensor data collected. The ultimate aim of our work is to harness the full potential of data science to provide system users with real-time, actionable information obtained from the raw sensor data collected.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.

A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.