PyTorch

Advanced course covering neural networks, CNN, RNN, and transformers with practical applications.

Completed project for CS6501: GPU Architectures at the University of Virginia. This research presents a comprehensive benchmarking study of matrix operation optimizations across NVIDIA GPU architectures, focusing on matrix transpose and multiplication. Through systematic evaluation of custom CUDA kernels and library implementations across RTX 2080 Ti and A100 GPUs, demonstrated that vectorized implementations achieve up to 6x speedup over naive approaches, reaching 1800 GB/s throughput on A100.

Completed for DS6051: Decoding Large Language Models at UVA, this project explores transformer-based methods for detecting AI-generated text in academic contexts. By fine-tuning RoBERTa using LoRA and optimizing for human accuracy, the model reduced false positives on human-written abstracts from 83.2% to just 0.7%, demonstrating the importance of fairness and robustness in detection systems.