Peter Adriaens

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Peter Adriaens, PhD, is Professor of Civil and Environmental Engineering, College of Engineering, Professor of Environment and Sustainability, School for Environment and Sustainability and Professor of Entrepreneurship, Stephen M Ross School of Business, at the University of Michigan, Ann Arbor.

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.

Ming Xu

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Ming Xu, PhD, is Associate Professor in the School of Environment and Sustainability with a secondary appointment as Associate Professor in the department of Civil and Environmental Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Xu’s 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).

 

Muzammil M. Hussain

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Muzammil M. Hussain is an Assistant Professor of Communication Studies, and Faculty Associate in the Institute for Social Research at the University of Michigan.

Dr. Hussain’s interdisciplinary research is at the intersections of global communication, comparative politics, and complexity studies. At Michigan, Professor Hussain teaches courses on research methods, digital politics, and global innovation. His published books include “Democracy’s Fourth Wave? Digital Media and the Arab Spring” (Oxford University Press, 2013), a cross-national comparative study of how digital media and information technologies have supported the opening-up of closed societies in the MENA, and “State Power 2.0: Authoritarian Entrenchment and Political Engagement Worldwide” (Ashgate Publishing, 2013), an international collection detailing how governments, both democracies and dictatorships, are working to close-down digital systems and environments around the world. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institution’s Issues in Technology and Innovation, The InterMedia Institute’s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism.

Twitter: @m_m_hussain.

Jerome P. Lynch

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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.

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.