My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies. Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.
My research focuses on developing and applying computational and data-enabled methodology in the broader area of sustainability. Main thrusts are as follows:
- 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.
- 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.
- 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://mingxugroup.org/
Henry Liu, PhD, is Professor of Civil and Environmental Engineering in the College of Engineering and holds a secondary appointment of Research Professor in the U-M Transportation Research Institute (UMTRI). Prof. Liu is also Director of the Center for Connected and Automated Transportation (Regional 5 UTC).
Prof. Liu’s research is in the areas of traffic network monitoring, modeling and control. His recent work has focused on traffic flow modeling and simulation, traffic signal control and optimization, traffic management under network disruptions and equilibrium traffic assignment. Prof. Liu is the co-founder and chairman of the advisory board for SMART Signal Technologies. The SMART Signal (Systematic Monitoring of Arterial Road Traffic Signals) is a real-time arterial performance monitoring system that uses traffic data from existing signal systems. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay and level of service. “Traffic engineers can use this information to improve traffic flow on roads controlled by traffic lights—reducing congestion and saving drivers both time and fuel. SMART Signal could also give drivers a more accurate prediction of travel times by accounting for time spent waiting at traffic lights,” states the University of Minnesota Intelligent Transportation Systems website. The system is now deployed at more than 100 intersections on major arterial corridors in Minnesota and Pasadena, California. Arterial corridors are roads where many cars can move from urban centers to high-capacity freeways.
Jerome P. Lynch, PhD, is Professor and Donald Malloure Department Chair of the Civil and Environmental Engineering Department in the College of Engineering in the University of Michigan, Ann Arbor.
Prof. Lynch’s 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, they collect from operational infrastructure systems, and undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, their 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 Prof. Lynch’s 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.