Danyka Byrnes, PhD

📧 danyka.byrnes@proton.me | 🌐 www.danykabyrnes.com
🔗 LinkedIn: /in/DanykaByrnes | 📂 GitHub: danykakbyrnes

Technical Skills

  • Programming and Analytics: Python (Pandas, NumPy, Geopandas, Scikit-Learn, Rasterio), R, MATLAB
  • Data Management: SQL, ETL
  • Data Visualization: Python (MatplotLib, Seaborn, Geopandas), InkScape, figure design for peer-reviewed articles
  • Statistical Analysis: Time series analysis, regression modeling, spatial statistics, machine learning, deep learning
  • Project Management and Team Work: Research team coordination, multi-year project planning, project documentation

Professional Experience

Researcher (Doctoral Candidate)

University of Waterloo - Waterloo, ON | 2020 - 2024

Project: Machine learning nitrogen load predictions

  • Quantified the total nitrogen mass stored in watersheds across the US from 1950 to 2017 using data-driven approaches
  • Developed and optimized Random Forest model achieving 97% prediction accuracy for nationwide riverine nitrogen loads, enabling scalable predictions for 2000+ unmeasured watersheds
  • Architected and implemented end-to-end Random Forest model pipeline integrating watershed characteristics, land use data, and nitrogen measurements from 400+ watersheds
  • Applied bias correction techniques and engineered features to capture geographical patterns and watershed characteristics, improving model performance by 25%
  • Developed data visualizations for peer-review journal articles to distill and communicate findings

Project: Data analytics of nitrogen input and riverine loading

  • Researched and analyzed key landscape drivers influencing nitrogen input and export patterns across U.S. watersheds
  • Engineered a comprehensive dataset integrating multiple disparate data sources across 400+ watersheds, implementing simple ETL pipelines in R and MATLAB
  • Implemented QAQC protocols using statistical methods to detect outliers and validate data quality
  • Built Weighted Regression on Time, Discharge, and Season (WRTDS) statistical model in R, processing over 100,000 water quality samples to predict daily nitrate concentrations
  • Conducted advanced statistical analysis to characterize relationships between watershed characteristics and nitrogen loading, improving our understanding of water quality responses to changing input

Project: TREND Nitrogen and Phosphorus datasets

  • Lead dataset developer for open-source nitrogen and phosphorus mass balance datasets, available on Figshare
  • Developed and maintained dataset with over 4 million elements, using an emperical model to convert USDA farming data into nutrient estimates
  • Harmonized datasets in different formats and varying spatial and temporal resolution using statistical tools and inferential variables to fill data gaps
  • Established version control protocols using git and documentation standards for collaborative open-source development, ensuring reproducibility

Agri-Environmental Science Development Assistant

Agriculture and Agri-Food Canada - Guelph, ON | Jan 2015 - Apr 2015

  • Conducted literary review to assemble a large dataset of varying nitrogen fertilizing rates with the corresponding corn yields
  • Established trends of Economic Optimum Nitrogen Rates to establish a relationship between fertilizer and yields to improve fertilizer recommendations
  • Gathered and compared the relationship between corn leaf optical data versus corn nitrogen requirements to improve decision support system (DSS) software

Teaching Assistant

University of Waterloo - Waterloo, ON | 2013, 2015, 2019

  • Collaborated with teaching teams to plan and deliver technical course material to 100+ students
  • Evaluated assignments and provided timely, individualized feedback on assignments and midterms
  • Mentored students on career development, resume writing, interview preparation, and stress management in academia

Synergistic Activities

Hydrology Section Student Committee Chair and Member

American Geophysical Union | 2020 - 2024

  • Led a team of 20 international volunteers to coordinated student-focused professional development initiatives
  • Organized and facilitated conference networking events for over 200 attendees at the AGU annual fall meeting
  • Advocated for student and marginalized members needs within AGU governance, resulting in expanded services and opportunities

‘Plotting Your Course with Effective Data Exploration’ Workshop Developer

Texas AMU Data Science Club | 2024

  • Designed and facilitated a hands-on workshop introducing students to exploratory data analysis and visualization techniques
  • Instructed participants on creating insightful data visualizations using Seaborn and Matplotlib, and applying unsupervised learning methods to extract patterns from complex datasets
  • Developed a reusable open-source workbook in Jupyter Notebook, version-controlled with Git and published for broader academic accessibility

Education

Doctorate (Civil Engineering)

University of Waterloo - Waterloo, ON | 2024

  • Relevant Coursework: Data Science Fundamentals, Process-Based Hydrological Modelling, Human-Natural Systems Modelling, Ecohydrological Modeling
  • Mentorship: Supervised seven undergraduate students hired to assist me on my projects

Master of Applied Science (Civil Engineering, Water Specilization)

University of Waterloo - Waterloo, ON | 2019

Bachelor of Applied Science (Environmental Engineering)

University of Waterloo - Waterloo, ON | 2017