To go directly to my projects, click here. Refer to the navigation bar on the left to see any of my portolfio pages!

About me

I recently finished up my MS in Computer Engineering at Duke University, where I gained a deep understanding of the underlying principles of computer systems, algorithms, and machine learning. I'm an aspiring ML engineer and make a point to spend time every day perfecting my craft and learning about advancements in the industry.

Throughout my academic career, I have focused on developing my skills in data analysis, machine learning, and DevOps/automation. I've worked on numerous projects exploring the intersection of these interests, which I hope to demonstrate through this portfolio. My expertise includes training, evaluating, and deploying machine learning models and completing end-to-end data science projects.

In addition to my academic pursuits, I've gained practical experience through my internship with the LA Clippers doing data analytics work (summers 2021 & 2022). You can read more about my time with the Clippers here.

As a dedicated and detail-oriented engineer, I'm committed to delivering high-quality solutions to complex problems. I'm passionate about using my skills to advance the field of machine learning and contribute to the development of new and innovative technologies.

In my free time, I enjoy keeping up with the latest developments in machine learning and attending industry events and conferences. I'm also somewhat of a basketball fanatic and find it to be a fascinating space for the application of ML. I'm excited about the opportunities that lie ahead and look forward to making a meaningful impact as a machine learning practitioner.

Undergraduate Studies

I started my undergraduate degree at Lasell University as a business major and a part of the men's basketball team. During my second semester, I took an introductory computing class and absolutely fell in love with the field of computer science. I then knew I wanted programming and analytics to be part of my career. I later transferred to Bryant University where I received a Bachelor's degree in data science with minors in business administration and mathematics. Outside of academics, I was part of the men's ultimate frisbee team and helped establish the school's first club basketball team.

Projects Summary

Below, I'll list some of my favorite projects I've had the opportunity to work on. Most projects have an associated demo showing how all the moving pieces work together. I'll also place a link to each repo in case you'd like to take a deeper look!

Image Classifier Web App

This is a web application that allows users to upload an image and receive a classification prediction for that image using a pre-trained deep learning model. The application uses Flask as the backend framework, which invokes an AWS Lambda function to make requests to a pre-deployed Amazon SageMaker endpoint for image classification. The predicted labels are then returned to the user and displayed on the web page.

Link to the project

Demo:

Improving Student Course Feedback through EEG Brain Signal Models

Our research focused on addressing the challenge of obtaining feedback on lesson clarity in online learning platforms. We built upon previous work by graduate student researchers at Carnegie Mellon University who designed a classifier using an EEG headset recorder device to detect student confusion.

Our objective was to reproduce and enhance their results by implementing a more rigorous model selection process, feature selection, scaling, and hyperparameter optimization using grid search cross-validation. Through these methods, we increased model performance by 27% and included an analysis to ensure our results were generalizable.

Link to the project
this project does not have a demo, but the slides and report can be found here.

Summarize CLI - a Hugging Face and AWS Sagemaker tool

A command line tool that takes a Hugging Face summarizer model (pegasus-xsum by default), deploys it to AWS Sagemaker, and queries it for inference. I made this tool utilizing Python, Bash, and a cool program named Bashly to help me develop the CLI arguments and flags. In the future, I'd like to add more functionality to use custom models and send multiple queries at once for longer texts.

Link to the project

Demo:

NBA Over/Under Prediction App

Streamlit app that automatically pulls newest data, predicts on it, and serves betting predictions to the user. Utilizes GitHub Actions for continuous integration and continuous delivery to AWS EC2. For this project, I used Python and a fairly simple Sci-kit Learn linear regression model (emphasis was more on the CI/CD than model tuning).

Link to the project

Demo:

Serverless Machine Learning Model

A linear model used to classify breast cancer instances using UCI'S breast cancer diagnostic data set. Model is trained using AWS Sagemaker and invoked using Amazon API Gateway and an AWS Lambda function.

Link to the project

Azure Databricks Cluster CLI

CLI built in Rust and Bash that allows you to provision custom clusters in Databricks using the Databricks REST API.

Link to the project

Demo:

Machine Learning API

FastAPI application that predicts Jayson Tatum's points per game based on continuously updated data from ESPN. Deploys automatically to AWS AppRunner.

Link to the project

Demo:

Finding Traumatic Brain Injury in CT Scans

For my image & video processing course at Duke, my teammates and I used multi-Otsu thresholding to segment potential instances of TBI from brain CT scans. This involved a variety of preprocessing steps to ensure consistency as well as researching and testing both machine learning methods and other segmentation methods to find an optimal solution.

Estimating the Impact of Opioid Control Policies

Along with a small group, I analyzed opioid-related policies across various states to determine whether they had an impact on both the prescription rates and overdose rates of opioids. This project includes a PowerPoint presentation going over our findings in a more succinct way.

Link to the project