Developed an Autograd engine implementing backpropagation (reverse-mode autodiff) over dynamically built DAGs forscalar operations, and built a PyTorch-style neural network library atop Micrograd

AI-powered system that tracks fridge inventory using computer vision, reduces food waste, and suggests recipes

Multithreaded Lock-Free Web Server in Rust

Efficient N-body Simulation using the Barnes-Hut Algorithm

Implemented a 2-Phase Commit Simulation in Rust

GPU Accelerated K-means Clustering Algorithm

Engineered a high-performance, multithreaded algorithm for identifying equivalent Binary Search Trees

Implemented Distributed Data Parallel training of VGG11 model on CIFAR-10 dataset using AWS SageMaker, analyzing scaling performance across multiple GPUs to balance parallelism vs. communication overhead in gradient synchronization

Applied LoRA for parameter-efficient fine-tuning of TinyLlama model on the guanaco dataset by injecting LoRA adapter modules into MLP linear layers, testing how training latency was affected by LoRA rank and module size

Quantized facebook/opt-1.3b model to 3-bits while preserving performance by protecting top 1% of salient weights through hardware-friendly scaling approach rather than mixed-precision, with ablation study revealing optimal scaling factors

Implemented a customized RAG model centered on the "Bhagavad Gita," optimizing output by referencing an authoritative knowledge base.