Dr. Cooley is Founder & President of NextGen Environmental Research Inc. in Winnipeg. Paul is an interdisciplinary scientist committed to innovation and effective decision making. Paul holds a Ph.D. in Geography from the University of Manitoba Center for Earth Observation Science with experience in land, water, and ice projects in sub-tropical, temperate, and sub-arctic environments. Paul’s scientific roots stemmed from ecology into geography and technology. Paul has a strong drive for innovation as evident in his R&D firms focus on satellite radar and drone technology.
James is pursuing a Masters Degree in Applied Science at the University of Manitoba’s Department of Civil Engineering. His current research focuses on advancing the field of fluvial ice cover roughness measurements. James has completed a Bachelors Degree in Applied Science (Environmental Engineering) at the University of Waterloo, and works remotely for the fluvial design and restoration firm GeoProcess Research Associates.
H. E. “Gene” Longenecker is a Preeminent Postdoctoral Scholar for Natural Hazards and Disasters in the College of Community Innovation and Education at the University of Central Florida’s School of Public Administration. In this role, Dr. Longenecker provides subject matter expertise and research on disaster risk, vulnerability, and resilience to the State of Texas’s General Land Office in support of $4.3 billion granted by the U.S. Department of Housing and Urban Development (HUD) for mitigation while developing research in the geographic aspects of natural hazards.
Prior to this, Dr. Longenecker was the Senior Physical Scientist for the Federal Emergency Management Agency (FEMA) in Washington, D.C., serving as principal advisor to the executive support function leadership group, comprised of top agency executives. Gene served many roles at FEMA, including as disaster analytics program manager, earthquake program manager, regional geospatial and remote sensing coordinator, and as the agency’s team lead for its Modeling Task Force. With his expertise in GIS, Dr. Longenecker led FEMA’s interagency modeling and perishable data collection campaigns through more than 30 natural disasters, from Hurricanes Katrina to Sandy to Maria, developing disaster impact analytics and multi-hazard risk and vulnerability assessments for real-time support of urban search and rescue, federal response planning, and FEMA risk reduction programs for earthquakes, floods, and hurricanes.
Dr. Longenecker has bachelor’s degrees in Geography and Philosophy (B.S.) from the University of South Alabama, and Master of Arts (M.A.) degree and Doctor of Philosophy (Ph.D.) degrees in Geography from the University of Colorado at Boulder. His doctoral research focused on risk, economic development, and vulnerability to flood hazards in engineered floodplains in the U.S., and Dr. Longenecker has organized and conducted numerous domestic and international training, technical, and conference workshops emphasizing the applications of GIS, modeling, data analysis, techniques development, and the general geographic and scientific principles supporting disaster management.
Hank is the Principal and Chief Technology Officer of Strategic Community Consulting.
Hank has over a decade of experience consulting to the Province of Manitoba and the Government of Canada on renewable energy, Lake Winnipeg Basin management, water quality trading, ecosystem services, bioeconomy development, and indigenous-led ecosystem management.
He uses an interdisciplinary approach to design natural infrastructure systems. His ability to link quantitative and qualitative techniques including high performance computing, big data and artificial intelligence (AI), reflect Hank’s creativity, intellectual rigour and skill in bringing new partnerships together to advance sustainability.
Scott Hamshaw is a research assistant professor of civil and environmental engineering at the University of Vermont. His research focuses on applying data-driven and machine learning methods to characterize erosion and suspended sediment transport in river systems. He also studies the use of new surveying technologies such as lidar and drone photogrammetry to monitor geomorphic change along rivers and teaches land surveying and mapping courses.
Dr. Christopher Henry (Ph.D., P.Eng.) is an Associate Professor in the Department of Applied Computer Science at the University of Winnipeg. Dr. Henry’s research focus is the development and application of theoretical frameworks for modelling human perception, such as quantifying the similarity of sets of objects and families of such sets – called computational proximity. This work is computationally complex, has led to his use of GPUs for general purpose computing (GPGPU) in his research since 2010, and has many similarities to the forms of pattern recognition used in machine learning. The result is that Dr. Henry’s research program lies at the intersection of computational proximity, GPGPU, and machine learning. His unique set of skills have led to many industrial collaborations working on problems such as hydrological modeling, remote sensing, customer profiling, sparse matrix operations, fluid simulations, and digital agriculture.
Dr. Storie is an Associate Professor and Chair of the Department of Geography at the University of Winnipeg, in Winnipeg, Manitoba, Canada. His area of specialization is in the use of GIS and Remote Sensing technologies to study how our urban environments are organized, how they change over time, and what impacts those changes have on both people and the landscape. His research involves the use of advanced geographic information systems integrated with satellite imaging software to analyze and fuse various geospatial datasets in order to derive land use/land use cover maps of urban environments. Working in collaboration with Dr. Henry, Dr. Storie has overseen the development of the satellite and classified products that will be used to train the deep learning network developed by Dr. Henry and his team. The results of this collaboration are neural networks that are capable of producing land use land cover from Landsat imagery.