I'm Joel Kim.

I'm a Computer Science and Data Science student at Northwestern University, passionate about building innovative software solutions and leveraging data to solve real-world problems.

I'm seeking full-time software engineering opportunities where I can apply my technical skills in machine learning, full-stack development, and data analysis to create impactful products.

View My Work Get In Touch Download Resume

My Projects

Spotify Playlist Quiz

A full-stack web application that creates personalized "Guess the Song" quizzes using the Spotify Web API. Features user authentication, dynamic playlist generation, and real-time scoring system.

REST APIs Flask MongoDB JavaScript HTML/CSS

GitHub

Airbnb Price Predictor

A machine learning model using gradient boosting to predict Airbnb rental prices based on location, amenities, and property features. Achieved 85% accuracy with comprehensive data preprocessing and feature engineering.

Python Pandas Scikit-learn XGBoost Data Analysis

GitHub

Digit Recognizer

Progressive neural network implementations for MNIST digit classification, starting from scratch with NumPy (94% accuracy), then PyTorch (97% accuracy), and finally ResNet-18 architecture (99% accuracy). Includes comprehensive analysis and comparison of different approaches.

Python NumPy PyTorch Deep Learning Computer Vision

GitHub Write-up (PDF)

No-Vig Calculator

A web application that converts American sportsbook odds to true probabilities by removing the bookmaker's vig (margin). Features real-time calculations, multiple odds formats, and deployed on Vercel for public access.

Python Flask HTML/CSS JavaScript Vercel

GitHub Website

+EV Odds Scraper

A web application that scrapes and compares sportsbook odds across multiple platforms to identify positive expected value betting opportunities. Features automated data collection, real-time odds comparison, and profit calculation algorithms.

Python Flask Web Scraping Data Analysis Vercel

GitHub Website

Sports Betting

Sports betting, or sports trading, is a fascinating and complex industry. On one hand, it’s a predatory market where sportsbooks profit off the losses of the average bettor. On the other, it's a market where those who approach it analytically can find inefficiencies and edges.

Over the past three years, I’ve gone on a long journey with sports betting. I’ve tested countless strategies, conducted extensive research, and engaged in discussions with peers, all while wagering tens of thousands of dollars.

I could talk about it for hours, but below is a view into my profit/loss over time, and some of the strategies I've used over the years.

A snapshot of my profit/loss trends over time.

Strategies I've employed:

Tailing Cappers: The starting point for many bettors copying bets from others, extremely dependant on who the bets come from.
News Updates: Placing bets based on anticipated news events or reacting quickly to breaking news to capitalize on market shifts, moderate variance.
Arbitrage: Exploiting pricing discrepancies between sportsbooks to generate risk-free profits, no real variance.
Positive Expected Value (+EV): Placing bets where statistical probabilities favor long-term profitability, moderate variance.
Correlated Parlays: Constructing multi-leg bets with interrelated events to maximize hidden value, higher variance.