Bryan R. Goldsmith

By |

Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

Mark Allison

By |

Mark Allison, PhD, is Assistant Professor of Computer Science in the department of Computer Science, Engineering and Physics at the University of Michigan-Flint.

Dr. Allison’s research pertains to the autonomic control of complex cyberphysical systems utilizing software models as first class artifacts. Domains being explored are microgrid energy management and unmanned aerial vehicles (UAVs) in swarms.

 

Brian Min

By |

Brian Min, PhD, is Associate Professor of Political Science in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor. Prof. Min holds secondary appointments as Research Associate Professor in the Center for Political Studies and the Institute for Social Research.

Prof. Min studies the political economy of development with an emphasis on distributive politics, public goods provision, and energy politics. His research uses high-resolution satellite imagery to study the distribution of electricity across and within the developing world. He has collaborated closely with the World Bank using satellite technologies and statistical algorithms to monitor electricity access in India and Africa, including the creation of a web platform to visualize twenty years of change in light output for every village in India (http://nightlights.io).

 

min-nightlights

Charu Chandra

By |

My research interests are in developing inter-disciplinary knowledge in System Informatics, as the basis for study of complex system problems with the fusion of theory, computation, and application components adopted from Systems and Informatics fields. In this framework, a complex system such as the supply chain is posited as a System-of-Systems; i.e., a collection of individual business entities organized as a composite system with their resources and capabilities pooled to obtain an interoperable and synergistic system, possessing common and shared goals and objectives. Informatics facilitates coordination and integration in the system by processing and sharing information among supply chain entities for improved decision-making.

A common theme of my research is the basic foundation of universality of system and the realization that what makes it unique is its environment. This has enabled to categorize problems, designs, models, methodologies, and solution techniques at macro and micro levels and develop innovative solutions by coordinating these levels in an integrated environment.

My goal is to study the efficacy of the body of knowledge available in Systems Theory, Information Science, Artificial Intelligence & Knowledge Management, Management Science, Industrial Engineering and Operations Research fields; applied uniquely to issues and problems of complex systems in the manufacturing and service sectors.

Theoretical work investigated by me in this research thrust relates to:

  • Developing Generalized System Taxonomies and Ontologies for complex systems management.
  • Experimenting with Problem Taxonomies for design and modeling efficiencies in complex system networks.
  • Developing methodologies, frameworks and reference models for complex systems management.
  • Computation and application development focused on developing algorithms and software development for:
    • Supply chain information system and knowledge library using Web-based technology as a dissemination tool.
    • Integration with Enterprise Resource Planning modules in SAP software.
    • Supply chain management problem-solving through application of problem specific simulation and optimization.

My research has extended to application domains in healthcare, textiles, automotive, and defense sectors. Problems and issues addressed relate to health care management, operationalizing of sustainability, energy conservation, global logistics management, mega-disaster recovery, humanitarian needs management, and entrepreneurship management.

Currently, my application focus is on expanding the breadth and depth of inquiry in the healthcare domain. Among the topics being investigated are: (1) the organization and structure of health care enterprises; and (2) operations and strategies that relate to management of critical success factors, such as costs, quality, innovation and technology adoption by health care providers. Two significant topics that I have chosen to study with regard to care for elderly patients suffering from chronic congestive heart failure and hypertension are: (1) the design of patient-centered health care delivery to improve quality of care; and (2) managing enhanced care costs due to readmission of these patients.

Data science applications: Real-time data processing in supply chains, Knowledge portals for decision-making in supply chains, information sharing for optimizing patient-centered healthcare delivery

Ming Xu

By |

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).

 

Pascal Van Hentenryck

By |

Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

His research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. He applies evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

Martin J. Strauss

By |

Martin J. Strauss, PhD, is Professor of Mathematics, College of Literature, Science, and the Arts and Professor of Electrical Engineering and Computer Science, College of Engineering, in the University of Michigan, Ann Arbor.

Prof. Strauss’ interests include randomized approximation algorithms for massive data sets, including, specifically, sublinear-time algorithms for sparse recovery in the Fourier and other domains.  Other interests include data privacy, including privacy of energy usage data.

Jon Lee

By |

Jon Lee, PhD, is the G. Lawton and Louise G. Johnson Professor of Engineering, Industrial & Operations Engineering, in the College of Engineering at the University of Michigan, Ann Arbor.

Jon’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems.