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Ruslan Nikolaev

I'm a Business Administration and Computer Science Student at the Wilfrid Laurier University and University of Waterloo , graduating in August 2020.

I'm also a Perception Engineering Intern at UBER ATG and Watonomous , working on building Autonomous Vehicles.

I work a lot on Data, AI and Computer Vision problems. I like to try out new things with various datasets on Kaggle. You can check out some of the Data Analytics and AI work that I have done below. I write on these topics and other tech related subjects from time-to-time on my Medium.

I also get to participate in international business case competitions representing the Lazaridis School of Business and Economics around the world.

Professional Experience:

Perception Engineering Intern, UBER ATG Fall, 2018 - Present.
Pittsburgh, PA.
- Starting soon

Perception Engineer, WATonomous Summer, 2018 - Present.
Waterloo, ON.
- Developing a traffic light and sign detector and classifier
- Developed a data aggregation pipeline to standardize numerous public object-detection datasets. In addition, created a command line interface to group and export images

Data Analytics Intern, Shopify Winter, 2018.
Ottawa, ON.
- Analyzed Data and built internal reports to understand consumer behaviour on the platform using SQL and Python.
- Built a dimensional model to aggregate data on daily platform usage improving query time by 20x.
- Developed features and build a customer maturity machine learning model to serve customized content to users and guide internal business decisions.

Software Lead, Waterloop Summer, 2017 - Summer 2018.
Waterloo, ON.
- Responsible for managing over 30 software developers to create and deliver innovative design for the embedded and software in the Hyperloop the pod.
- Wrote a research paper on the topic of Embedded-Systems in Hyperloop Pod describing design decisions in the system. Currently in publishing stage.
- Created a Node.js web-socket server for bi-directional streaming of data from sensors on the pod to a group of remote devices, that allow to control the pod in real time.

Software Engineering Intern, Adknown Winter, 2017.
Guelph, ON.
- Full-Stack Development of time tracking Jira plugin, used Jira APIs to effectively integrate time tracking into development process to improve the speed of development process.
- Researching and Developing web-push notifications and a serverless client to send out batch notifications.
- Creating various interactive and mobile-friendly interstitials.

Python Developer Intern, TIONIX Summer 2016.
Kazan, Russia.
- Part of development team working on Russian Cloud Systems solution at TionixLabs.
- Designed an autocomplete algorithm in Python to improve customer experience when controlling the cloud system through a command line interface.
- Working with open source projects such as: OpenStack, Django, Sphinx, Pymongo to build a complete suite of cloud software and services such as Virtual Desktop and Compute Intances.

Research Papers

Software Systems in Hyperloop Pod
Overview of the Software System developed to control the pod during the high-speed launch in the in the annual SpaceX Hyperloop Pod competition. Reliance of redundant multi-nodal command execution with RAFT consensus algorithm and an on-board CAN network to enable modular design and data collection from Pod's sensors.

Programming Projects

How Much @QHacks
Solving optimization problem to find best meal for the money near the user. Tomato API integration for restaurant menu listing and Price Discrimination Algo. All packaged into a mobile app.

Tryify @Decode Hackathon
In partnership with Shopify, instagram-feed-like shopping experience Shopify merchants's products. Ruby backend with intelligent product search and Shopify checkout API integrations.

Medium API package
Medium's API to get all posts made by a user or a publisher. Over 100 downloads.

Data Science Projects

Drake Lyric Generation
Generating Drake Style Lyrics using LSTMs and Language Models. Over 20 forks on GitHub and 5,000 reads on Medium.

US Mass Shootings Analytics
Extensive visualization and statistical analysis of historical data on Mass Shootings in US.

Driver Face + Eye Tracking
YOLO and Haar Cascades face and eye detection and tracking CV pipeline. Open/closed eyes Deep Net classifier, critical component for safe AV testing.

ML-Utils library
Machine Learning utilities library for Python, simple image labelling web-tool

Snack Classifier
Solving problem of uneducated trips to the snack shelf in the office using IoT and Computer Vision. CNN for snack classification.

Accolades

1st. Solvers Cup 2018
International business case competition in Budapest, Solvers Cup'18

1st. @UNICC 2018
International business case competition in Spain, UNICC'18

Wilfrid Laurier Delegate @JDC Central 2017
Selected to represent Laurier's Digital Strategy team at the JDCC business case competition

1st. @RuHacks 2017
Amazon Go Implementation

1st. @Qhacks 2017
How Much, in money saving category

1st. @StudentXel Case Competition 2016
Marketing Case for Klarity Pills

Semifinalist. @PepsiCo. Pitch competition 2016
New Venture Idea Pitch competition