Steven Rud

About Me

I am a recent Brandeis University Graduate with a Bachelor of Science in Computer Science. I've always admired Mathematics and the Sciences for their intrigue, purity, and application in the world. I have culminated my interest in these two fields into the study of Computer Science. I enjoy Computer Science for the process of logically and creatively problem solving various complex tasks to reach an end goal in a project with limitless possible potential and real life application.

GitHub

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Education Education Header Image

B.S. in Computer Science, Brandeis University 2020 - 2024

I have undergone a heavy courseload in Computer Science consisting of the study of Software Engineering techniques, Algorithms, Data Structures, OOP, Machine Learning, Artificial Intelligence, Natural Language Processing, Cybersecurity, Operating Systems, and Data Science.

Projects Project Header Image

Project 8

Mastermind Game Web App

Developed a full-featured Web App Mastermind game using React, Vite, and AI tools, supporting Solo, local Pass-and-Play, and Vs AI game modes with a shared board, 10-guess combined limit, and real-time turn tracking across all players. Implemented three AI difficulty tiers: random guessing, heuristic candidate filtering, and Knuth's minimax algorithm. Built a polished dark UI with jewel-tone animated pegs, configurable palette size and duplicate-color rules, session stats persisted via Zustand, a procedural Web Audio API sound engine, and colorblind mode View Project Web App Link

Project 5

Popcorn Palace Movie Ticket Booking System

I built a movie ticket booking backend service using Java and Spring Boot, which exposes a RESTful API and supports CRUD operations for movies, showtimes, and ticket bookings, backed by an H2 in-memory database. Also added centralized error handling, ensured that overlapping showtimes and duplicate seat bookings are prevented, and wrote comprehensive JUnit and Mockito tests. View Project

Project 7

Species Genome Analysis and Animal Group Predictor

This project conducts species genome analysis using machine learning, Bioinformatics, and Data Science techniques. It parses genomic sequences into K-mers, analyzes codon frequencies, and trains a model to classify species and predict animal groups. Visualizations like heatmaps and classification reports support the analysis. The model successfully identifies the animal group of mystery genetic sequences and handles unknown genetic data effectively. View Project

Project 2

LEGO Set Finder Website

This full-stack web application developed using JavaScript, HTML/CSS, Node.js, MongoDB for data storage, and SQL for data cleaning and preparation, serves the purpose of assisting users in finding and choosing LEGO sets. Utilizes SQL to create and facilitate a dataset of LEGO sets from 1964 to 2021 with over 5,500 sets. Offers a range of filters to streamline searching and allows users to keep track of sets they want and own. View Project

Project 4

Letterboxd Top 4 Movie Generator and Yearly Movie Data Analysis

This Full-Stack project was created with a Python backend and a React.js frontend with Flask and Dash libraries to create a web application that provides users with a personalized top 4 selection of their top-rated movies on Letterboxd, along with a data analysis feature showcasing the top 4 movies and the average ratings over the years. View Project

Project 1

Custom Pac-Man Game Maker

I developed the ability to create and play a custom Pac-Man game in Java using object-oriented programming and design with full traditional game logic, board management, character movement, and UI controls. It contains an interactive map editor that supports custom element placement, dynamic board resizing, and a default balanced map layout. And it has engineered advanced ghost movement logic to track Pac-Man, respawn mechanics, and integrated win/lose conditions with power-up features. View Project

Project 3

Attention-Based Neural Machine Translation from English to Pig-Latin

This program was created in Python using Natural Language Processing techniques. This project trains an attention-based neural machine model to translate from English to Pig-Latin. Next, I developed a Transformer Decoder integrating causal and regular scaled dot-product attention, and visualized attention weights at each decoding step to analyze token focus during generation. Adapted from Brandeis University Fundamentals of NLP course with more NLP work in the course repositories. View Project NLP I Repository NLP II Repository

Skills Skills Icon

Skill 1

Python

Skill 2

Java

Skill 3

JavaScript

Skill 5

React

Skill 6

HTML & CSS

Skill 7

MongoDB

Skill 8

SQL

Skill 9

Node.js

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