nim Composition Space Optimization is a high-performance tool leveraging metaprogramming to implement several methods for selecting components (data dimensions) in compositional datasets, as to optimize the data availability and density for applications such as machine learning.

A Python tool to identify and remove similar-looking images from a dataset. Utilizes image preprocessing and hashing techniques for efficient comparison.

⚡ Vectro: Lightning-fast embedding quantization. Hit 12M+ vec/s throughput (4.85× faster than FAISS C++) while drastically cutting memory footprint for vector databases and local AI research.

Highlighting expertise in data migration, data normalization and standardization, this project demonstrates successful data transfer from Snowflake to Databricks. It emphasizes optimized data flow and enhanced accessibility through standardization, showcasing a commitment to ethical data practices.

A python tool to optimise file size of archived family memories

Morphia is a powerful library designed for transforming complex JSON structures into compact, index-based arrays and back. Ideal for efficient data serialization and deserialization, Morphia simplifies the handling of nested and dynamic data by flattening JSON objects into arrays and reconstructing them with precision.

An award-winning solution proposal for optimizing vertiport site selection in Urban Air Mobility (UAM) using multi-source data analysis and platform logic design. Recognized at the 9th Hyundai Motors University Case Competition.

interface to ADAO (Data Assimilation and Optimization features in Python ) in SALOME

Contains my project that analyzes air quality sensor data to determine if the NO (Nitric Oxide) sensor in N. Mai, Los Angeles, CA can be removed without affecting data accuracy.

UI website for the ESAT Team 5A2 Smart Grid Data Project

Lobster data analysis and modelling using Python

Official Tensorflow implementation for ActiveTCR: Active Learning Framwork for Cost-Effective TCR-Epitope Binding Affinity Prediction

This explores data optimization using PySpark, covering big data concepts, pipelines, machine learning, and data transformation techniques. The goal is to enhance data processing efficiency and model performance.

fast dataset merging and deduplication tool

Production Delta Lake Medallion architecture. Orchestrates Bronze→Silver→Gold pipeline with PII masking, deduplication, MERGE/Upsert, OPTIMIZE/VACUUM, and Airflow DAGs.

Fullstack Developer for NEV/MOSY | Specializing in Extreme Data Optimization & Minification (PI-API Developer). TypeScript/JavaScript Architect.

AutoSQL — Self-optimizing SQL query pipeline. Automatically rewrites slow queries, measures speedup, and keeps improvements. One file, zero dependencies, free API.

Movie discovery web app built with Next.js, TypeScript, and Tailwind CSS. Browse trending movies, search instantly, explore detailed movie pages, and enjoy a modern responsive UI inspired by streaming platforms.

This project analyzes historical weather data to identify patterns and predict future weather conditions, focusing on extreme events and temperature trends across Europe.

A feature optimization module for high-dimensional datasets (bioinformatics-leaning). It automates variance filtering and backward elimination to remove redundant features while preserving meaningful predictive signal. Designed to boost classifier performance, interpretability, and efficiency.