About Me
Hi!


Contact

phone icon 347-946-5304

phone iconN.Gordon.prodev@gmail.com

phone iconNeil-G


Interests

I'm a full-stack developer interested in JavaScript, interfaces, and machine learning and statistics.

Lately I've been loving React and Flux architectures, interactive data visualizations using SVG and D3, and providing desktop/server/mobile applications and solutions using JavaScript (other languages welcome of course).


Professional Skills

Languages

JavaScript (ES5/6), React, Flux, Redux, Python, Ruby, Rails

Design

HTML, CSS, Sketch 3, UI/UX, Responsive Design, Data Visualization, SVG, D3

Development Tools

Linux, Git, Webpack, Test-driven development (TDD & BDD), Information Architecture, SQL, noSQL, Statistics, Machine Learning, Computer Vision


Experience

Enchanted Diamonds

Full-stack Engineer

March '15 - Present
  • Led a team of 3 in the designing and building of a single-page content management system to manage inventory and activity related to over 1 million diamonds and pieces of jewelry and over $1M in monthly orders.
  • Used computer vision, data mining techniques, and statistical methods to classify and compare diamonds by their facet map structural representations, resulting in the creation of new sales products.
  • Established company best practice and style guide documents for feature development and rapid prototyping, including UX, UI, and React application architecture and development.

BX200 (Bronx Visual Artist Directory)

Frontend Developer

Nov '13 - March '15
  • Maintained updates to site content for new events and artists. Included maintaining and updating htm, css, and media content.
  • Monitored visitor activity and growth using Clicky Web Analytics and gave biweekly reports.

CUNY City College of New York Math Dept.

Researcher

Dec '14 - August '14
  • Created unsupervised machine learning scripts to perform computer vision categorization. Scripts were used to detect and classify hand-written digits.
  • Tested and compared different neural network models, examining differences in activation functions, correction step size, and layer number and size.
  • Achieved over 98% classification accuracy on NYU MNIST handwritten data set

Projects

phone iconThink Quick!

Realtime multiplayer game based on solving arithmetic problems.

phone iconResearch Reviewer

Mobile-friendly application to document, review, plan, and share summaries from reading and research.

phone iconFacial Reconstruction

A machine learning and computer vision project where 11,000 facial images were used to train an algorithm to reconstruct 1,200 images of incomplete faces


Education

CUNY City College of New York

Bachelor of Science, Applied Mathematics

2011 - 2013