Dhruv Yadav

Improved documentation clarity for 10M+ monthly PEFT users by authoring a Hadamard product decomposition overview and LoHa vs. LoRA comparison guide. Accelerated developer onboarding by adding a Stable Diffusion UNet quickstart example, with PR #3224 merged by core maintainer BenjaminBossan.

Improved ML pipeline processing efficiency by 20% across 10K+ real-world records by engineering automated data cleaning, exploratory analysis, and preprocessing workflows. Increased model prediction accuracy by 15% and reduced end-to-end analysis time by 20% through optimized feature selection, model tuning, and evaluation pipelines.

Built responsive web applications using HTML, CSS, JavaScript, and Django/Flask, implementing reusable components and backend integrations. Collaborated with a 3–5 member development team to deliver production-ready features.

Increased data analysis efficiency by 15% across predictive modeling tasks by integrating end-to-end ML workflows using Python, Pandas, and TensorFlow. Optimized preprocessing, feature engineering, and validation pipelines.

Developed modular and reusable UI components using HTML, CSS, and JavaScript, while handling end-to-end feature development. Maintained version control with Git and GitHub.

Pursuing a B.Tech in Computer Science Engineering with a focus on Artificial Intelligence and Machine Learning.