
B.A., Environmental Business, University of Redlands, Redlands, California, 2016
Coursera AI for Everyone (2018)
MATLAB Deep Learning Onramp (2018)
Hazardous Waste Operations and Emergency Response 40-Hour Certification (2019; refreshers 2020 to present)
Basic Offshore Safety Induction and Emergency Training (including Helicopter Underwater Escape Training and Emergency Breathing System) (2020)
First Aid and CPR certified (current)
Transportation Worker Identification Credential (current)
Member of Young Environmental Professionals
Conner Schultz Project Scientist (206) 957-0363 seattle, WA cschultz@integral-corp.com
Mr. Conner Schultz is an environmental consultant with experience in data collection, analysis, and management and in GIS. He extends his use of applications, such as ArcGIS and QGIS, with additional programming languages that include SQL and MATLAB®, to perform quantitative analyses and to develop supporting data visualizations. Mr. Schultz assists in analyzing both spatial and non-spatial data for a variety of projects, including environmental risk assessments, natural resource damage assessments, and RI/FSs. Since joining Integral in early 2019, he has been a contributing member to Integral’s sediment-profile and plan view imaging (SPI–PV) field survey team. He supports SPI–PV field surveys, and conducts PV image analysis and SPI–PV data post-processing. He also has performed analyses to support the development of custom image processing algorithms using deep learning and machine learning methodologies. In addition, Mr. Schultz has served as a project manager for three projects with responsibilities that include cost management, resource allocation, and successful deliverable tracking and client communication.
- Renewable Energy
- Sampling
- Site Assessment
- Modeling
- GIS Design
- Ecological Modeling
- Project Management
- Groundwater Sampling
- Ocean Modeling
- Site Investigation
Renewable Energy
Sampling
Site Assessment
Modeling
GIS Design
Ecological Modeling
Project Management
Groundwater Sampling
Ocean Modeling
Site Investigation
Sackmann, B., G. Revelas, K. Whitehead, C. Schultz, and C. Jones. 2020. Artificial intelligence and computer vision for cost-effective benthic habitat characterizations. Poster presentation at the Ocean Sciences Meeting. Co-sponsored by the American Geophysical Union, the Association for the Sciences of Limnology and Oceanography, and The Oceanography Society, San Diego, CA. February 16–21.