High performance algorithms in C#: SIMD/SSE, multi-core and faster

A project analyzing the performance of Linear and Binary search algorithms through execution time and memory usage metrics, comparing efficiency across various dataset sizes. Demonstrates practical applications of algorithm optimization and performance profiling in Rust.

This is a web-based app that I developed with Dash framework and Python in 2021 as a course project in the Artificial Intelligence Graduate Program at the University of San Diego. The objective is to use the dataset from diabetes labs to train a Machine Learning model to predict diabetes based on diagnostic measurements.

This is a repository of the code used for the experimental work in my Bachelor thesis on Approximation Algorithms for Graph Edit Distance (GED). It includes implementations, benchmarking scripts, and evaluation methods for comparing GED approximation algorithms with exact computations.