Brian Weeks

Brian Weeks

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In the Weeks lab, we work at the intersection of ecology and evolutionary biology to try to understand how large scale biodiversity patterns arose, and what they might tell us about how natural systems will respond to human activities. We have a particular focus on the impacts of climate change on birds, and are increasingly using computer vision tools to measure bird traits on large numbers of photographs of museum skeletal specimens. This new approach has enabled us to generate skeletal trait datasets at an unprecedented scale that have begun to reveal some fascinating patterns in bird morphology that we are using to understand biotic responses to global change.

Joshua P Newell

Joshua P Newell

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I work in the area of urban sustainability, with research questions at multiple scales and environmental and socio-economic systems. My work uses spatial analysis (esp. GIS and remote sensing) and mass-balance accounting (life cycle assessment, material flow analysis). My lab is starting to use big data from a range of sources (Zillow, Twitter, etc) and I am interested in collaborating with data sciences of various stripes on sustainability and equity challenges.


Accomplishments and Awards

Karen Alofs

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My research focuses on how environmental change, including climate, invasion and habitat destruction influences freshwater ecological communities across space and time. I am involved in a collaborative interdisciplinary project funded by a MIDAS Propelling Original Data Science (PODS) Grant: CHANGES: Collections, Heterogeneous data, And Next Generation Ecological Studies.We are developing protocols for integrating heterogeneous natural science datasets to investigate the impacts of environmental changes on species. Our project focuses on climate change impacts on inland lake fish communities across Michigan, drawing on more than a century’s worth of data and specimens archived at the University of Michigan Museum of Zoology (UMMZ) and the Institute for Fisheries Research (IFR), which is a cooperative unit of the Michigan Department of Natural Resources (DNR) Fisheries Division and the University of Michigan.

Neil Carter

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Carter’s research combines quantitative, theoretical, and field approaches to address challenging local to global wildlife conservation issues in the Anthropocene. His work includes projects on endangered species conservation in human-dominated areas of Nepal, post-war recovery of wildlife in Mozambique, human-wildlife coexistence in the American West, and the effects of artificial lights and human-made noise on wildlife habitat across the contiguous US. Research methods focus on: (1) spatializing both human and wildlife processes, (2) probabilistic methods to infer human-wildlife interactions (3) simulation models of coupled natural-human systems, and (4) forecasting and decision-support tools.

Kathleen M Bergen

Kathleen M Bergen

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Kathleen M Bergen, PhD, is Associate Research Scientist in the School for Environment and Sustainability at the University of Michigan, Ann Arbor. Dr. Bergen currently has interim administrative oversight of the SEAS Environmental Spatial Analysis Laboratory (ESALab) and is interim Director of the campus-wide Graduate Certificate Program in Spatial Analysis.

Prof. Bergen works in the areas of human dimensions of environmental change; remote sensing, GIS and biodiversity informatics; and environmental health and informatics. Her focus is on combining field and geospatial data and methods to study the pattern and process of ecological systems, biodiversity and health. She also strives to build bridges between science and social science to understand the implications of human actions on the social and natural systems of which we are a part. She teaches courses in Remote Sensing and Geographic Information Systems. Formerly she served as a founding member of the UM LIbrary’s MIRLYN implementation team, directed the University Map Collection, and set up the M-Link reference information network.

Ming Xu

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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 contains 405 industries. 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/

William Currie

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Bill Currie studies how physical, chemical, and ecological processes work together in the functioning of ecosystems such as forests and wetlands.  He studies how human impacts and management alter key ecosystem responses including nutrient retention, carbon storage, plant species interactions, and plant productivity.   Dr. Currie uses computer models of ecosystems, including models in which he leads the development team, to explore ecosystem function across the spectrum from wildland to heavily human-impacted systems.  He often works in collaborative groups where a model is used to provide synthesis.  

He is committed to the idea that researchers must work together across traditional fields to address the complex environmental and sustainability issues of the 21st century.  He collaborates with field ecologists, geographers, remote sensing scientists, hydrologists, and land management professionals.


Accomplishments and Awards

Arun Agrawal

Arun Agrawal

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My research seeks to leverage survey, census, remote-sensed, and citizen-science datasets to address social and collective dilemmas related to climate mitigation and adaptation, vulnerability to climate risks, the relationship between climate change and health, the unfolding trajectories of demographic change in conjunction with climate change and sociopolitical stability, commoning and commoning-based interventions in the context of socio-cultural and social-ecological systems, and post-disaster recovery. I am particularly interested in techniques that help harmonize datasets from different sources, support causal inference from observational datasets, and identify causal mechanisms underpinning associational relationships.

After an undergraduate degree in history and an MBA, I found myself most intrigued and interested by questions related to why people strive together, how they achieve shared purpose, and how knowledge about collaborative actions – commoning – can help address the most persistent challenges confronting societies. Much of my research is founded on this wellspring of unresolved social questions and dilemmas.

Some of my most interesting projects

Climate change is transforming the landscape and background of sociopolitical and social-ecological relationships. Advances in data sciences promise radical improvements in data harmonization and analysis of observational datasets to support causal inference necessary for improved decision making. Our research focuses in particular on how such advances will enable deeper knowledge and better choices for improved health, sustained peace, and living in harmony with nature in the context of climate, socio-demographic, and institutional changes.

The most significant scientific contribution I would like to make

Strengthen the human capacity to act together to achieve shared purpose

What makes me excited about my data science and AI research

The possibility of identifying and learning unsuspected and unrecognized patterns in joint work for shared purpose.

Some interesting facts about myself

Much of the late summer and fall finds me hunting for edible mushrooms in the woods in and around Ann Arbor
I believe the most interesting + useful chemical processes are those that yield delicious tastes in the kitchen.


Accomplishments and Awards

 


Research Highlights