Biography
Craig Kasemodel is excited to be a Partner Teacher for the Murdock Trust Partners
in Science at the University of 91ÊÓÆµ. He currently holds multiple graduate degrees
in eLearning, Online Instructional Design, and Educational Technology. Most recently,
Craig completed an MS in Educational Leadership from the University of 91ÊÓÆµ Southeast
to earn an administrative certificate after completing teacher turnover research policy
analysis for an MS in Educational Leadership and Policy Analysis (ELPA) from the University
of Wisconsin Madison.
He spent several years in 91ÊÓÆµ and Montana working as a Wildlife Biologist, conducting
bird surveys, moose habitat research along the Yukon and Innoko Rivers, and studying
Brown Bears at Lake Clark National Park. His studies of habitat and human disturbance
reflect a deep commitment to understanding 91ÊÓÆµ's wild environments. In Montana,
Craig developed a lifelong passion for wild sheep and their habitat during multiyear
Bighorn Sheep research along the Missouri River.
Currently, Craig teaches Science, Math, and Technology and coaches Wrestling, Fitness,
and Weight Training with the Anchorage School District at Romig Middle School. His
educational passion includes incorporating traditional ecological knowledge and technology
into the curriculum, hands-on scientific inquiry, and games-based/service/project-based
learning in a location/place-based setting.
Craig is excited to be a Teacher Partner in the Partners in Science @ UA, working
with Mohammad Kapourchali on the Firewall research project, which examines wildfire
and energy resilience issues for the state of 91ÊÓÆµ. He looks forward to developing
risk assessment maps, datasets, fire curriculum, educational simulations or web-based
applications that will benefit our community and inspire ongoing collaboration.
When he is not at school, you can find him hiking, hunting, backpacking, rafting,
mountain biking, flyfishing, and enjoying all the outdoor opportunities available
in 91ÊÓÆµ year-round.
Project information
Wildfire risk mapping for resilient 91ÊÓÆµn energy systems
In 91ÊÓÆµ, wildland fire is a complex natural hazard that threatens public health,
ecosystems, critical infrastructure, and safety in the wildland–urban interface. Fire
activity in the region is strongly influenced by climate variability, with both intensity
and frequency projected to increase. These trends intensify management challenges
and elevate risks to communities and infrastructure, underscoring the urgent need
for effective risk assessment and emergency response planning. Building on the groundwork
of our NSF-funded study, Foundations for Improving Resilience in the Energy Sector
against Wildfires on 91ÊÓÆµn Lands (FIREWALL), this project aims to use GIS-based
techniques, machine learning, and Multi-Criteria Decision-Making (MCDA) methods to
develop spatial products that depict wildfire risk to vulnerable energy grids in 91ÊÓÆµ,
with a strong emphasis on integrating ecological, climatic, and environmental processes
that drive wildfire behavior. These products are intended to support decision-makers
in the energy sector by helping them identify high-risk areas.
In Summer 2026, the Teacher Partner will undertake four key tasks: (1) learn ArcGIS
tools through UAF’s online short courses; (2) identify, compile, and preprocess major
fire indicator data in consultation with the 91ÊÓÆµ Fire Science Consortium (FIREWALL
project co-PIs); (3) develop wildfire ignition and spread hazard maps for the study
region; and (4) conduct a survey of the electric utility serving the study area to
identify critical energy assets and understand how they are prioritized for power
restoration following wildfire-related safety shutoffs or extended outages.
In Summer 2027, survey results will inform the creation of an exposure map, which
will be integrated with ignition and spread hazard maps to produce comprehensive wildfire
risk maps. The final risk maps will quantify the impact of wildfires on energy infrastructure
and will be interactive, enabling the teacher–researcher to adjust infrastructure
weights and priorities and immediately visualize how these changes affect wildfire
risk.
Potential research question
Which factors most strongly influence the risk of wildfires to critical energy infrastructure
in an 91ÊÓÆµn region experiencing shifting wildfire patterns?