Software Developer

JONATHAN DONESKY

Phone: 301-***-**** Email: adkfmq@r.postjobfree.com

LinkedIn: /jonathan-donesky-539000127

Github: /jdonesky

Technical Skills

Front end

• React/Next.js, Redux,

ES6/JavaScript, Typescript

• HTML5, CSS, Materialize, EJS

Back end

• Python, Node, Express,

GraphQL (Apollo)

Databases

• MySQL, Firebase, MongoDB,

AWS Elastic Beanstalk, S3

DevOps

• Git, Github, Docker,

Kubernetes, Jenkins

Testing

• Jest, Enzyme

Data Science & Machine Learning

• Keras, Tensorflow, Pytorch

Software Projects

JSCRATCH - Software used: Typescript, React (hooks), Redux, Express.js, esbuild, Wasm, Webpack, npm January 2020 In-browser coding environment with real-time bundling and transpiling of ES6, JSX and CSS

• Achieved lightning-fast bundling and transpiling in-browser by customizing plugins to esbuild and Wasm

• Integrated advanced IDE features such as syntax highlighting, autocomplete, and code linting using Monaco Editor.

• Integrated custom redux middleware containing debouncing logic to optimize performance on save-data actions

• Reduced latency through caching queried modules in browser’s indexedDB.

• Added security layer isolating execution of user-provided code in iframes and restricting communication with the parent.

• Streamlined dependency management using multi-package architecture with symlinks. DUMB FACEBOOK - Software used: React (16.8 - Hooks), Redux, React-router, Firebase, Webpack, npm December 2020 Single page web app mimicking styles and functionality of Facebook (link)

• Combined Firebase Realtime database and Cloud functions to create reactive, real-time data storage.

• Designed and implemented persistent data layer using Redux and localStorage.

• Reduced page load latency through code-splitting using higher order components and dynamic imports.

• Integrated APIs such as Google Maps, providing location search autocomplete and geocoding with a responsive map interface.

• Leveraged Github Actions in CI/CD pipeline.

FLASH - Software used: Python, Keras, Tensorflow, Pandas, numpy, scikit-learn, matplotlib Jan 2020 - April 2020 Long Short-Term Memory Recurrent Neural Network, models flash-flooding in Ellicott City, MD

• Used LSTM layers to capture time-lagged flood factors and reduce backpropagation of errors.

• Reduced noise by interpolating for missing and false stream sensor readings in training data.

• Prevented overfitting by incorporating dropout regularization (algorithm probabilistically excluding weight updates). Work Experience

SOFTWARE ENGINEER INTERN • NASA Goddard Space Flight Center • Greenbelt, Maryland Jan 2020 - April 2020

● Developed a Long-Short Term Memory (LSTM) neural network using Keras and Tensorflow to forecast flash floods and improve evacuation warning systems in Ellicott City, MD.

● Increased forecast accuracy by 14% (Nash-Sutcliffe Efficiency score) using model sensitivity analysis to determine optimal combination of environmental inputs.

● Created data pipeline with Python/Pandas to integrate, process, and visualize 10 years of satellite and in situ sensor readings. PROJECT MANAGER • Laisar Management Group • Silver Spring, Maryland Nov 2018 - April 2019 / Nov 2019 - Feb 2020

● Managed technology research, supply chain analysis, and regulatory compliance for Offshore Wind developer Ørsted.

● Applied domain knowledge to develop software for tracking employee demographics, work duties, and hours. Education

B.S. ENVIRONMENTAL SCIENCE AND POLICY • University of Maryland • College Park 2017 – 2020

● Honors: magna cum laude (GPA 3.96/4.0)

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