Experience

Software Engineer

2017-2018

Onist Technologies

  • Collaborated in building and documenting a rest-api that performs CRUD operations on Mongo databases.

    Stack: Node, Serverless, Koa, Typescript, AWS (Lambda, Codebuild, Cloudwatch, Cloudfront, Route 53), MongoDB, Mongoose, Swagger

  • Collaborated in building the MVP version of an IOS application that enables user of Onist platform to access some of its functionalities on their smartphones.

    Stack: React-Native, Redux, Typescript, Observables, Websockets

  • Developed a nightly cron job on AWS to automatically sync users' information from Onist databases into Mailchimp though its api.

    Stack: Node, Typescript, AWS (Lambda, Codebuild, Cloudwatch), MongoDB, Mongoose, GraphQL

  • Set up the CI/CD pipeline for a new web application from development environment to QA and production.

    Stack: AWS (Codebuild, S3, Cloudfront, API Gateway, Route 53)

  • Collaborated in maintaining a legacy code by fixing bugs and adding necessary new functionalities.

    Stack: Javascript, Meteor, MongoDB

  • Collaborated in developing and maintaining end-to-end tests for Onist web application to automate sanity testing scenarios.

    Stack: Cucumber/Gherkin, Typescript

Mentor, Front-End Fundamentals

2018-Present

Lighthouse Labs

  • Explained fundamental concepts of front-end web development, HTML, CSS, JavaScript, and JQuery.
  • Provided small-group and one-on-one assistance to students in completing class works and projects.
  • Explained basics of git, GitHub, and code versioning.
  • Familiarized students with effective debugging and troubleshooting techniques.

Research Fellow

2010-2015

Department of Mechanical and Materials Engineering, Queen’s University

Turbulence Simulation and Modelling Laboratory (TSM LAB)

  • Collaborated with teams of computational physicists to extend a parallel in-house flow solver (Python, F90) and develop a spectral flow solver (MATLAB).
  • Developed several post-processing codes using advanced statistical methods to systematically analyze the collected data from a variety of angles.
  • Parallelized (with MPI) some of the in-house post-processing codes to speed up the data analysis process through employing more than one processor at a time.
  • Performed Numerical Simulations and collected 120 TB of simulation data and 5 GB of data from various sources in literature and, then, thoroughly examined and filtered datasets to disregard redundant data and extract the required information.
  • Determined a hidden trend and came up with an explanation for a long-standing open-question in a flow (wall-jet) which improves the accuracy of industrial models in predicting drag force of this flow by 25%.
  • Translated the results of statistical analysis to physical description of the flow through data visualization (tecplot, Matlab, gnuplot) and published the results in journals and conferences.
  • Led a group of 5 Computational Fluid Dynamics (CFD) specialist to develop post-processing codes to extract data from three datasets (10 TB) and make an animation to show the difference between three turbulence models to non-CFD experts.