More coming soon!

Graduate Work

My research in the robust systems lab at Northeastern University focuses primarily on activity recognition in video data using deep learning.


1. Action Recognition Applied in an Airport Setting

Our research is applied in the airport security domain. This is a final project for a machine learning class that explains the difficulty in applying traditional computer vision techniques in the domain of airport security. My thesis research in part attempts to overcome some of the hurdles witnessed in the course of this project.

2. Action Recognition Lecture

Due to my expertise in action recognition I was asked by my professor to give a lecture alongside a peer in my advanced computer vision course.

Data Visualization

3. Database for Historic US Election Campaigns

This is a database I designed for a final project in my database management systems class. The database tracks position statements of candidates and election results for historic US elections throughout the 1960s.

See my most recent post for a video demo!

Code available here.

4. Bike Sharing Data Analysis

For a data visualization course, my team used a regression to model the viability of proposed locations for the BlueBike company of boston which used US censor data, the google api, and publically available data from the BlueBike company of Boston.

Announcements for new locations were measured as high viability by our model suggesting our intuition matches market research.

Computer Vision & Machine Learning Coursework

The following are a collection of projects I did that are directly related to my degree.

5. Spatial & Temporal Video Filtering

6. Combining Images with Shared Landmarks

7. Circulant Tracking in Video Data

8. Extra Credit Project for EECE5639

Undergraduate Work

1. Improving Music Genre Classification

This is the last project I did at Northeastern. It was for a graduate level elective in Machine Learning. This counted as the final grade in the course. I was partnered with a long time friend and colleague – Eric. We compared results of classification using text data from lyrics against using signal data from audio samples of songs of different genres. Eric studied a classifier dealing with the features extracted from audio samples of songs while I studied a classifier targeted at the songs’ lyrics. As a final step in the project we combined the two approaches we studied into a neural network that achieved a moderate increase in accuracy relative to both individual methods.

Together we got an A on the project.

2. Project Karman

This is the final project for my Senior Design project – known at Northeastern University as a Capstone Project. As seniors six of us designed and implemented an avionics board to be used as the first revision of an embedded avionics board that could be utilized in an unmanned rocket. The project serving as a proof of concept went on to be a pivotal component of the local chapter of AIAA’s attempt to be the first academic institution to breach the Karman line. In this club activity I continued on as the lead firmware developer for the project and taught many peers at Northeastern University in topics ranging from firmware development to project management. Under AIAA sponsorship several more device drivers were incorporated into the design and the communication system for the avionics system matured as well.

3. A Colony of Intelligence

This is a research paper I wrote for a technical writing class while getting my degree. The paper revolves around the growing influence that biology has in the field of artificial intelligence. Many of the points being made are still relevant today!

Personal Projects

1. Python Tool set for CV Applications

Contributing to R&D in a computer vision lab, you get tired of writing the same lines of code over and over again. This is a repository filled with scripts for data processing, and other common computer vision scenarios I wanted all in one place.

2. Project Ophelia

This project is meant to simulate the performance of a trading algorithm over actual bitcoin data – but can be tweaked to work with any cryptocurrency/commodity!