Machine Learning and Software Development Projects

I'm an Computer Vision Engineer with a passion for deep learning. Some of the my projects I've worked on are shown below.

GitHub Blog Startup I formally worked at: AIthElite

At KEF Robotics, I've led the development of a pipeline for creating "Digital Twins" of objects through the use of Gaussian Splatting.

Personal Project
Gaussian Splatting PyTorch

At KEF Robotics, I've fine-tuned monocular depth estimation networks and applied them to autonomous flight. Recently a new SOTA network from TikTok was released called Depth-Anything. I can't share my work projects but I did make a huggingface space to try the Depth-Anything model on your own video files!

Personal Project
Monocular Depth Estimation PyTorch

GPT4Readability is a powerful package that leverages large language models (LLMs) and a vector database to generate a file and suggest code improvements for your Python code repositories.

Personal Project
NLP LangChain

Less than 24 hour hackathon for tech in healthcare. SkinsAI stands for Skin Key Imaging Nursing System, and it's a free-access diagnosis tool for classifying moles as benign or malignant. SkinsAI's binary classification model uses a small convolutional neural network. The purpose is to provide a free, easy-to-use, anonymous and fast tool to give patients a first diagnosis, as well as a website where useful and up-to-date information can be gathered. We were awarded 2nd place overall out of 29 teams ($2,000 cash prize).

Pitt Challenge Hackathon
PyTorch Django Computer Vision

Competition for predicting the aqueous solubility class (high, medium, low) of small molecules. I applied a graph neural network called SolTranNet to solve the problem.


A GUI that uses machine learning techniques such a CNN classifier and YOLO object detection to tell a user if they have correctly signed a letter in American sign language.

Hack the Northeast
Tensorflow tkinter Computer Vision

State of the art disparity map generation from 3D stereo images. This was a group project in the Introduction to Deep Learning course at Carnegie Mellon University.

Pytorch Computer Vision

Question answering model. This was a group project in the Natural Language Processing course at Carnegie Mellon University.


Used CNNs to build an end-to-end face verification system. This was an independent homework in the Introduction to Deep Learning course at Carnegie Mellon University.

Pytorch Computer Vision

My team and I were given a dataset and tasked with predicting the water level of the Rhine river. The model with the best results was Vector Auto Regression (VAR).

statsmodels Time-Series-Analysis

Identifying the phoneme state label for each frame in the test data set. The utterances were of variable length.

Pytorch NLP

I used a combination of Recurrent Neural Networks (RNNs) (more specifically BLSTMs) and Dense Networks to design a system for speech to text transcription.

Pytorch NLP

A series of machine learning models applied to different problems. All built purely with NumPy (no machine learning packages used).


Built a Deep Neural Network classifier to predict the stay duration of a patient based on general parameters about the hospital and the severity of the illness.

Pytorch tkinter

I built a python package that can be used to search through jupyter notebooks at or below a specified directory.

Python Package
Jupyter Notebook