Project

This project implements a Machine Learning (ML) pipeline for the Adver-City synthetic dataset. The dataset is designed for investigating cooperative perception in autonomous vehicles. Overall, this project enables efficient, reproducible modelling of the Adver-City dataset through ML workflows. We have chosen to train a model to identify weather conditions (clear, fog, rain) from nighttime images. Note: full GitRepo available here. Figure 1: Example Scenario (foggy) from the Adver-City dataset. The dataset contains images of a variety of weather conditions (clear, fog, rain) and times of day (day, night). In this project we focus on classifying weather conditions from nighttime images. Operation The project is organized into four main stages. Depending on what your goal is (data exploration, ingestion, or training), the notebooks indicated below describe how to run each stage of the workflow. ...

A fully open architecture implementation of modern multi-agent coordination techniques. All agents make individual decisions based on local information only. There is no global plan. This is an open project I use to explore new ideas, validate theoretical results, and produce data for my academic research. ...

Building on my earlier exploration of custom ChatGPTs, I tried building an AI app from scratch, primarily using vibe coding supported by GitHub Copilot in VSCode. Inspired by a recent Airbnb experience, I developed ContactsnApp: a user-friendly application designed to streamline the extraction and annotation of phone numbers from images using AI models. Comments from the Co-author I asked ChatGPT how the project went. Here it is in her own words: ...