Rei Meguro

I enjoy the art of over-engineering complex, fast things

About Me

I'm an ambitious Computer Science student who enjoys automating systems and workflows. I work extensively with AI technologies (machine learning, LLMs, computer vision) as well as full-stack development. I am also the author of Japan's strongest chess engine, Raphael.

Note: I am comfortable using LLMs as development tools, but I do not rely on unverified output. Any LLM-generated code used during development is carefully reviewed and tested by me before being included in a project.

My Recent Projects

Click on the cards to visit project links

Raphael

Raphael is a chess engine written in C++ and is currently the strongest chess engine in Japan.

It uses alpha-beta search with multiple enhancements, along with a quantized neural network running on the CPU using SIMD intrinsics, trained on tens of millions of self-play games.

Onyx

Onyx is an open-source AI platform that automatically ingests documents from connected sources and uses deep research agents to answer questions or complete complex tasks.

I contributed to the development of the deep research agent and the knowledge graph indexing and inference pipeline, eliminating OOM errors, improving fault tolerance, and tripling indexing speed.

AdaPhish

AdaPhish is an adaptive phishing detection and submission platform that learns attack patterns over time using a vector store and RAG, built with Docker, FastAPI, Next.js, and Nginx.

The project was published at the 4th IEEE ICAIC Conference and received the Best Paper Award.

Kineval

Kineval is a Python-based interactive learning tool for forward kinematics, inverse kinematics, collision detection, and RRT motion planning.

It was developed for EECS 367 at the University of Michigan and is structured to allow students to implement these algorithms themselves without needing to manage rendering or other supporting systems.

Folio

Folio is a startup platform that connects freelancers with venture-backed startups.

I developed the core email notification system and contributed to data migration pipelines used to transition from the previous platform, working with Django, Next.js, Docker, and Git.

ConverseCart Search Engine

ConverseCart is a startup that helps retail stores improve product click-through rates by providing an AI-powered search engine.

I contributed to early versions of the search system using word embeddings, bag-of-words models, and cross-encoders to enable fast, personalized natural language queries.

RTD Toolkit

RTD, developed at the University of Michigan, enables provably safe motion planning while accounting for uncertainty in measurements using polynomial zonotopes.

I worked for the ROAHM Lab to port the MATLAB implementation into a general-purpose Python library, while also introducing performance optimizations and improving usability.

Mandelbrot Fractal Explorer

An interactive Mandelbrot fractal explorer written in C++ using SFML.

It uses thread pools to exploit the embarrassingly parallel nature of the computation, enabling 1080p rendering and zoom levels up to 10^-16.

Sudoku Solver

A Sudoku solver that combines a backtracking algorithm for solving, a CNN for digit recognition, and PyAutoGUI for automated number entry.

The system reads the puzzle from the screen, recognizes digits, computes the solution, and fills the grid automatically.

Nerd Wrecker: Connect 4

A fully automated multiplayer Connect 4 player.

The program uses alpha-beta search with bitboards to efficiently compute optimal moves, and leverages PyAutoGUI to read game states and input moves through the graphical interface.

LC2K CPU Simulator

LC2K (Little Computer 2000) is a minimal instruction set architecture with eight instructions, used in the University of Michigan EECS 370 Computer Organization course.

This project implements the LC2K CPU along with a memory-mapped display inside Excel, using self-referencing cells to simulate the clock, registers, and memory.

Plaite

Plaite is a nutrition tracking application that uses YOLOv8 for real-time food detection, developed during the 24-hour MHacks 2024 hackathon. It also includes an LLM-based food recommendation assistant.

The project received the Best Use of Intel AI Award.