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Curriculum Vitae

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Experience

Geospatial Data Scientist

Leidos Research Support Team

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April 2024 - Present (Scientist II)

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April 2020 - August 2022 (Scientist I)

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Supporting the National Energy Technology Laboratory, Department of Energy

  • ​Supported safe and sustainable energy production by integrating geospatial and machine learning methods to explore relationships between environmental hazards and offshore oil & gas infrastructure for developing solutions to energy-related challenges.

  • Utilized expert knowledge, data science techniques, and database management to collect and process vast amounts of data ranging from hazard assessment, oil and gas production, and meteorological and oceanographic conditions to fit the needs of data-driven, big data analytics.

  • Trained, tested, and validated a gradient boosted regression tree algorithm that analyzes over 1,000 variables to quantify the lifespan of offshore infrastructure with 97% accuracy. A manuscript reporting these results was accepted to Marine Structures (Dyer, et al., 2022).

  • Developed a comprehensive machine learning (ML) workflow using residual networks for semantic segmentation to locate submarine landslides in high resolution bathymetry data, followed with spatial analytics and neural networks to forecast landslide susceptibility. Achieved an accelerated workflow for ML-informed offshore geohazard research using High Performance Computing. 

  • Coordinated and collaborated with a multidisciplinary team with efficient Python programming methods to curate and manipulate an extensive body of heterogeneous PostgreSQL database, then integrated into NETL’s online Common Operating Platform for emergency management and policy planning.

  • Participated in science communication including three presentations at academic conferences, writing a peer-reviewed journal publication and a technical report, and assisting in three proposal reviews.

Graduate Research Assistant

Portland State University

 

Sept. 2022 - June 2024

  • Conducting research and applied science on forest resilience, resistance, and recovery following major wildfire disturbances under climate change in the Pacific Northwest with multi-scale approaches pertaining to spatial analysis, remote sensing, and data science.

  • Processed over 500 GB of satellite imagery using parallel processing in the R programming language to create spatially accurate and temporally consistent spatial and non-spatial products for performing forest change analyses. Following forest classification models developed using a Random Forest model achieved a greater than 95% accuracy.

  • Performed advanced spatial analysis to study landscape-level patterns of post-fire seed dispersal including distance-weighted nearest neighbor, global and local Moran's I, and spatial regression.

  • Working towards completing a NSF funded masters thesis which seeks to expand the current knowledge of how post-fire delayed tree mortality influences forest dynamics following severe wildfire events in Oregon’s western Cascades and contribute to forest management decisions for supporting healthy forests. 

  • Supporting the operations of the Global Environmental Change Laboratory through lab management, social media coordination, mentorship of undergraduates, and presentations to both academia and government entities.

Research Fellow

Oak Ridge Institute for Science & Education

 

Feb. 2019 - April 2020

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Supporting the National Energy Technology Laboratory, Department of Energy

  • Conducted advanced research using spatio-temporal analytics, scripting, and machine learning to produce models for evaluating offshore infrastructure lifespan and estimating potential risks related to oil/natural gas development.

  • Produced Python scripts to increase data processing efficiency, curate a toolbox for spatial and non-spatial data manipulation, and manage big data sets including Environmental Sensitivity Index and AIS ship track line data. Curated a GDAL utility toolset and uploaded to GitHub.

  • Assisted in the writing process for technical reports by creating graphs and maps, articulating a professional and scientific writing style, and offering edits and comments throughout the paper.

  • Attended the AGU Ocean Sciences 2020 conference in San Diego on behalf of ORISE and NETL. Gave an oral presentation on an offshore infrastructure risk assessment project and networked with other scientists.

Drone Remote Sensing Intern

North Willamette Research and Extension Center, Oregon State University

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Feb. 2019 - April 2020

  • Implemented and managed the preparation, set up, and flying operations of a small UAV for remote sensing data collection, all while ensuring compliance with FAA regulations.

  • Set up and managed field flights for the small UAV with TAF report checks, pre-flight checklists, ground control points, and pre-planned flight paths. Checked for quality of data collected prior to leaving the site by examining photos captured. 

  • Assembled multispectral data with Pix4Dmapper to develop vegetation index rasters for independent vegetation health research that analyzed the relationship between reflectance properties and crop health for increasing crop yields.

  • Counseled with farmers and agricultural researchers on the possibilities and benefits for drones to improve the bridge between remote sensing applications and precision agriculture.

Education

Oregon State University

Sept. 2015 - March 2019

3.67 GPA

Corvallis, OR

Bachelor of Science cum laude in Environmental Sciences

     Specialization in Conservation, Natural Resources, and Sustainability

Geographic Information Sciences (GIS) Certificate

Portland State University

Masters of Science in Geography

Sept. 2022 - June 2024

Portland, OR

Skills

  • Spatio-temporal statistics and analysis

  • Machine learning applications

  • Data sciences: big data acquisition, processing, and analysis

  • Cartography

  • Interdisciplinary teamwork experience

  • Communication skills, both written and verbal

  • Small UAV flying for research purposes

Tools

  • Proficient with ArcGIS Pro, ArcGIS Online, Pycharm, Jupyter, GitHub, Microsoft Office, EndNote, ENVI, and Pix4Dmapper

  • Effective scripting in Python for statistical, geospatial, and machine learning analyses: GDAL, ArcPy, NetCDF4, scikit-learn, PyTorch, Matplotlib, Numpy, Pandas, SciPy, CSV

  • Experience with High Performance Computing (HPC)

Peer-Reviewed Publications

Dyer, A.S., Mark-Moser, M., Duran, R., & Bauer, J. (2024). Offshore Application of Landslide Susceptibility Mapping using Gradient-Boosted Decision Trees: A Gulf of Mexico Case Study. Natural Hazards. https://doi.org/10.1007/s11069-024-06492-6. 

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Dyer, A.S., Zaengle, D., Nelson, J., Duran, R., Wenzlick, M., Wingo, P., Bauer, J., K. Rose, Romeo, L. (2022). Applied Machine Learning Model Comparison: Predicting Offshore Infrastructure Integrity with Gradient Boosting Algorithms and Neural Networks. Marine Structures. https://doi.org/10.1016/j.marstruc.2021.103152. 

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Dyer, A.S., Busby, S., Evers, C., Reilly, M., Zuspan, A., & Holz, A. (in prep., 2024). Ecological implications of post-fire delayed tree mortality following high severity wildfire in Oregon’s western Cascades. Targeting Landscape Ecology.

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Lackey, G., Dyer, A.S., & Pfander, I. (in prep., 2024). Forecasting Well Integrity Issues in an Active Oil and Gas Field Using Widely-Available Well Information. TargetingScience of the Total Environment.

Technical Reports

Nelson, J., Dyer, A., Romeo, L., Wenzlick, M., Zaengle, D., Duran, R., Sabbatino, M., Wingo, P., Barkhurst, A., Rose, K., Bauer, J. (2020). Evaluating Offshore Infrastructure Integrity. DOE/NETL-2021/2643; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR. p 70. https://doi.org/10.2172/1780656.

Conference Publications & Presentations

Dyer, A. Spatio-Temporal Patterns and Drivers of Fire Refugia in the Western Oregon Cascades. Oregon Post-fire Research and Monitoring Symposium. February 7-9, 2022. Oral presentation. 

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Dyer, A., Duran, R., Mark-Moser, M., Rose, K., Bauer, J., Zaengle. D., Wingo, P. Geohazard Analysis of Seafloor Landslide Potential for Infrastructure Protection. Esri User Conference. July 12-16, 2021. Virtual presentation.

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Romeo, L., Dyer, A., Bauer, J., Barkhurst, A., Duran, R., Nelson, J., Sabbatino, M., Wenzlick, M., Wingo, P., Zaengle, D. and Rose, K. 2021. Forecasting Offshore Platform Integrity: Applying Machine Learning Algorithms to Quantify Lifespan and Mitigate Risk. Machine Learning in Oil & Gas. April 15, 2021. Virtual presentation.

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Dyer, A., Zaengle, D., Mark-Moser, M., Duran, R., Suhag, A., Rose, K., Bauer, J. Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry. AGU Annual Fall Meeting (Virtual), 2020. Session: NH007 - Data Science and Machine Learning for Natural Hazard Sciences II Posters. Virtual poster presentation.

 

Duran, R.,  Dyer, A.,  Mark-Moser, M.,  Bauer, J., Rose, K., Zaengle. D., Wingo, P. A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico. AGU Annual Meeting 2020, Session:  NH010 - Geohazards in Marine and Lacustrine Environments. Virtual presentation.

 

Romeo, L., Dyer, A., Zaengle, D., Nelson, J., Wenzlick, M., Duran, R., Sabbatino, M., Wingo, P., Barkhurst, A., Bauer, J., Rose, K. 2020. Assessing Current and Future Infrastructure Hazards: Forecasting Integrity using Machine Learning and Advanced Analytics. Oil and Gas Project Review Meeting. October 26, 2020. Virtual presentation.

 

Justman D., Romeo, L., Barkhurst, A., Bauer, J., Duran, R., Dyer, A., Nelson, J., Sabbatino, M., Wingo, P., Wenzlick, M., Zaengle, D., Rose, K. invited talk. Advanced geospatial analytics and machine learning for offshore and onshore oil & natural gas infrastructure. GIS Week 2020. October 6-7, 2020. Virtual presentation.

 

Romeo, L., Nelson, J., Dyer, A., Wenzlick, M., Zaengle, D., Sabbatino, M., Wingo, P., Duran, R., Barkhurst, A., Rose, K., Bauer, J. in prep. Assembling and Curating a Multi-Facet Offshore Platform Database with Open-Source Data and Records for Advanced Infrastructure Integrity Evaluation. DOE Data Days (D3) 2020. October 5-7, 2020. Virtual presentation.

 

Dyer, A., Romeo, L., Wenzlick, M., Bauer, J., Nelson, J., Duran, R., Zaengle, D., Wingo, P., Sabbatino, M. Accepted. Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas. Esri User Conference, San Diego, CA, July 13-15, 2020. Oral presentation.

 

Dyer, A., Rose, K., Bauer, J., Romeo, L., Barkhurst, A., Wingo, P., Sabbatino, M., Nelson, J., Wenzlick, M. Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas, AGU Ocean Sciences Meeting. February 16-21 2020. Oral presentation.

 

Romeo, L., Dyer, A., Nelson, J., Bauer, J., Rose, K., Dao, A., Wingo, P., Sabbatino, M. Building Regional Baselines and a Suite of Spatial Tools to Better Prepare for Oil Spills, AGU Ocean Sciences Meeting. February 16-21 2020. Poster presentation.

 

Romeo, L., Dyer, A., Bauer, J. Geospatial Research Science at NETL. Oregon State University Geography Day. November 13, 2019. Invited to give an oral presentation. 


Romeo, L., Wenzlick, M., Dyer, A., Sabbatino, M., Wingo, P., Nelson, J., Barkhurst, A., Bauer, J., Rose, K. Building Data-Driven Analytical Approaches and Tools to Evaluate Offshore Infrastructure Integrity. Carbon Capture, Utilization, Storage, and Oil & Gas Technologies Integrated Review Meeting, 2019. Poster presentation.

Professional Development

Deep Learning – Specialization through Coursera

(1) Neural Networks and Deep Learning Improving Deep Neural Networks, (2) Hyperparameter Tuning, Regularization, and Optimization, (3) Structuring Machine Learning Projects, (4) Convolutional Neural Networks, (5) Sequence Models

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American Geophysical Union – Member

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Introduction to Python – developed by P. Wingo

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Aeronautical Knowledge Test – Federal Aviation Administration Remote Pilot Certification

May 2020 – August 2020

July 2020 - Present

June 2019 – August 2019; Feb. 2021 – March 2021

June 2018

Achievements

TechConnect National Innovation Awardee - TechConnect World Innovation Conference and Expo

NETL’s Advanced Infrastructure Integrity Model (AIIM) was recognized as a top-ranked innovation and selected as a TechConnect National Innovation Awardee.

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Engagement in Environmental Sciences Scholarship – Oregon State University

Awarded to 4 students each year in recognition of co-curricular activities in the environmental science field outside the classroom and academic achievements in the classroom.

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Outstanding Acacian Scholarship – Acacia Fraternity

Awarded to 5 students a term who are voted by the chapter and meet requirements for GPA, volunteer hours, and house involvement.

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Ling-Stout Scholarship – Acacia Fraternity – two time recipient

Awarded to one member a term who excel in academics, leadership, and human service while promoting the ideas and objective of acacia.

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Provost Scholarship – Oregon State University

Non-resident freshman undergraduate scholarship based on academic excellence in high school.

June 2022

March 2018

January 2019

May 2018  & Aug. 2018

Sept. 2015

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