This blog post explores the three-stage bias detection process in DeepEval, an LLM-based evaluation system that quantifies bias in AI-generated text. The methodology leverages structured validation, templated prompts, and a scoring framework to assess bias across multiple categories.
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.
As part of my Deep Learning course, this project explores the use of convolutional and recurrent neural networks for Speech Emotion Recognition (SER). Using the RAVDESS and TESS datasets, we train models to classify emotions from audio signals, aiming to improve human-computer interaction, mental health applications, and AI-driven affective computing.