Featured Projects
Physics-Based Reinforcement Learning Environment: rigidRLFeatured
A custom 2D rigid body mechanics engine built in C++ with direct Python bindings. Designed to simplify the integration of physics simulations into reinforcement learning workflows. It supports articulated agents via joint constraints and provides a transparent API for creating environments, managing state observations, and bridging the gap between low level physics implementation and high level training logic.
A Deep Learning Framework from Scratch: forgeNN Featured
Sole author of a deep learning framework developed in Python/NumPy, published on PyPI (pip install forgeNN). The framework features a dynamic computation graph with reverse-mode automatic differentiation, highly vectorized tensor operations, and a full suite of modules for building modern neural networks including transformers.
OTOKON and ITURO Website Remake
Redesigned and developed the official websites for OTOKON (otokon.org) and ITURO (ituro.org) using HTML, CSS, and JavaScript. The new designs focus on improved user experience, mobile responsiveness, and modern aesthetics to better represent the organizations' missions and activities.
Monocular Visual Odometry Pipeline
A classical computer vision system built from scratch in Python and OpenCV to estimate a camera's motion and reconstruct its trajectory from a sequence of images, a foundational component for SLAM in GPS-denied environments.
C++ Accelerated Inference Engine
A high-performance C++ backend for a Python-trained neural network. This project bridges the gap between Python prototyping and the speed required for real-time applications by moving core matrix operations to a release-optimized C++ build.
UAV Telemetry Anomaly Detection
An unsupervised deep learning system to monitor multi-variate sensor data in real-time and flag critical flight anomalies. The system uses a specialist LSTM Autoencoder trained on normal flight data to learn a signature of healthy system behavior.