osdev
12-01-2024 -- Present
Technologies: x86 Assembly
- WIP
- Developed a minimal operating system from scratch using Assembly, and C.
- Focused on low-level system programming and hardware interaction.
veri-nn
10-01-2024 -- 11-01-2024
Technologies: Verilog, ModelSim, PyTorch
- Engineered a high-performance neural network library in Verilog with modular, scalable components, meeting 4MB on-chip memory constraints for deploying ML architectures on a DE1-SoC FPGA, achieving 2.27ms inference speed.
- Trained a neural network in PyTorch, utilizing quantization techniques to optimize memory usage with int32 precision, achieving 80% validation accuracy on the MNIST dataset.
hydrogen-compiler
09-01-2024 -- Present
Technologies: C, C++, x86 Assembly
- WIP
- Developed a compiler for a custom programming language.
- Implemented lexical analysis, parsing, and code generation.
phishing-reel
08-01-2024 -- 08-31-2024
Technologies: Python, Jupyter Notebook
- NewHacks 2024
- Developed a phishing email detection system using transformers.
- Analyzed email content and metadata to identify phishing attempts.
flow-sense
08-01-2024 -- 08-31-2024
Technologies: React, Django, PostgreSQL, GPT-3.5
- HackThe6ix 2024
- Developed a reading tool that enhances productivity by integrating LLM API-based word definitions and inline note-taking, reducing research time by 40%.
- Successfully preserved the original report formatting in PDF processing, maintaining 95% accuracy in layout retention.
- Built and deployed within a 36-hour period during Hack the 6ix 2024, achieving a functional MVP with time to spare for feature enhancements.
deepfake-audio-detection
07-01-2024 -- Present
Technologies: Python, Jupyter Notebook, PyTorch
- WIP
- Developed a deepfake audio detection model using machine learning techniques.
- Analyzed audio features and trained models to distinguish between real and fake audio.
robot-line-challenge
06-01-2024 -- 07-01-2024
Technologies: C++
- Implemented a robot line-following algorithm in C++.
- Focused on sensor data processing and motor control.
mini-torch
05-01-2024 -- Present
Technologies: C++, CUDA, LLVM
- WIP
- Recreated PyTorch’s object-oriented design with C++ and CUDA, implementing core functionalities such as LinearLayer, ReLU, Softmax, and CrossEntropyLoss, enabling an intuitive setup for deep learning training loops similar to higher-level languages.
- Optimized CUDA kernel functions for matrix multiplication through memory coalescing and tiling, and further utilizing kernel fusion with ReLU, to achieve a 5x reduction in training time.
image-captioning
05-01-2024 -- Present
Technologies: Python, PyTorch
- WIP
- Engineered a deep learning model combining Computer Vision and NLP, using PyTorch to integrate CNNs with Vision Transformers/LSTMs, achieving a max BLEU-4 caption score of 11.0.
- Designed models using transfer learning with InceptionV3 and VGG16, improving BLEU caption scores by 0.2.
dish (dope interactive shell)
05-01-2024 -- Present
Technologies: C
- WIP
- Built a shell program in C with command parsing and execution capabilities.
- Implemented tokenizer function and lexing function
trash-talk
01-01-2024 -- 01-31-2024
Technologies: Arduino, Flask, HTML, CSS, JavaScript
- UTRAHacks 2024
- Developed an Arduino-based waste sorting system that accurately categorizes trash into garbage or compostable materials, achieving a sorting accuracy of approximately 85%.
- Integrated a web interface with real-time data visualization, reducing manual sorting time by 50% for users.
- Implemented hardware components such as an ultrasonic sensor and moisture sensor, optimizing the sorting mechanism to handle up to 30 items per minute.
deep-learning-from-scratch
12-01-2023 -- Present
Technologies: C/C++, CUDA, Python, PyTorch, NumPy
- Spearheaded Neural Networks, CNNs, RNNs, LSTMs, and Transformers from scratch using OOP principles, increasing code implementation efficiency by 50%.
- Programmed implementations to utilize CUDA acceleration using NVIDIA’s C++ libraries, resulting in a 5x speedup in training.
- Produced educational resources with practical examples, instructing over 5+ individuals on deep learning theory.
ai-art-detection
12-01-2023 -- 06-01-2024
Technologies: Python, PyTorch, NumPy
- Co-developed a CNN-based deep learning model for AI-art detection using PyTorch, achieving a maximum 90% test accuracy.
- Leveraged transfer learning with pre-trained models such as VGG16 and ResNet50, reducing model loss by 6%.
- Evaluated 10+ architectures proposed in research papers to identify the best-performing model.